Search

Supply chain management can lead to a sustainable competitive advantage. The present study is in search of paper industry supply chain strategies with respect to supply chain dynamics and lean supply chain using dynamics in order to gain a competitive advantage in Iran Wood and Paper Industries Company (Chooka). For this purpose, first, using organizational data and decision makers' participation, the system dynamics model was designed and after validation, the model was simulated in a ten-year horizon. According to the behavior of target variables and model sensitivity analysis in the simulation horizon, policies in line with lean supply chain strategy and sustainable profitability strategy of Chooka business were designed and applied separately and in combination to the model. And analyzed. According to the findings of the model simulation, productivity promotion policies through the use of lean methods in internal processes, increasing the quality of paper products, increasing innovation in the production and supply of paper products, improving raw material supply management And strategic partnership with suppliers of raw materials, industrial waste management, waste and solid waste management and staff empowerment have been presented as the best combined policies of the supply chain strategy of Iran's wood and paper industries.

The policy of changing direct subsidiary to indirect subsidiary was one of the main government policies and the government according to forth development program should organize the subsidiary system in cultural parts with the approach of changing subsidiary payments for production to consumption. During recent years the subsidiary of paper distribution for newspapers (as the most important output of cultural and political production) always have been indirectly and via subsidiary for press and this has caused numerous problems for paper industry. This survey is the analysis of economic and commercial policies of government in industry of paper for newspapers. Effective elements in the domestic industry of papers for newspapers have been sorted by using questionnaire and interview and by PESTI model. In this article supportive policies of government is recognized as the main effect on paper industry and discussed completely and finally, some proper solutions are offered.

Nowadays quantitative methods have become very important tools for forecasting purposes in markets as for improved decisions and investment. Forecasting accuracy is one of the most important factors involved in selecting method; Artificial Neural Network (ANNs) arc flexible computing frameworks that can be applied to a wide range of forecasting with a high degree of accuracy, in this research, Fuzzy logic and Artificial Neural Network are integrated in to the Fuzzy Back- Propagation Network (FBPN) for sales forecasting in Wood and Paper industry. The proposed system is evaluated through the real world data provided by a Wood an d Paper company and experimental results indicate that the Fuzzy Back- Propagation approach outperforms are better other two different forecasting models (Linear Regression and ARIMA time series model) in MAPE measures.

One of the most common problems, encountered in the quality of product industrial units, includes low level & high distribution of production quality specifications. The complexity of methods causes the manager not to be able to make the right decision for solving this problem. The Six Sigma methodology is a powerful tool for adjusting the parameters of production processes & its optimization in order to make them competitive products. To be able to guide the quality changes of goods in the desirable direction, first we must become familiar with these changes & then we must recognize the reasons of their presence. In this regard, barcode system which has high capabilities in increasing the speed & precision of data transfer can help the organization to reach a desirable level of sigma. The present article has attempted to familiarize the readers with the definition of six sigma methodology & how to use the barcode system to improve the sigma level in a case study. The quality improvement of products which is a result of optimized management of sigma level has resulted in achieving desirable economical goals for the organization. These goals have been assessed in the conclusion section.

In combined cycle power plants, instead of releasing gases produced from burning fossil fuels, after turning the gas turbines, they enter into heat recovery steam generator (HRSG) boilers to produce steam. The produced steam by these boilers is used to generate electricity in steam turbines and thus, electricity generation efficiency is dramatically increased. In this way, the efficiency of electricity production increases significantly. These boilers are made at a great cost and also, any failures of them cause a power plant to stop and create enormous costs, so optimizing their reliability is very important. This paper deals with the modeling of the HRSG feed water system by using a block diagram for two states (i.e., half-time and full load), to evaluate the difference between the proposed alternative designs, by considering their reliability. The method used in this paper can be applied to evaluate and optimize the reliability of many other industrial systems. Introduction In power generation, the reliability of industrial control systems is crucial, as failures can disrupt services, leading to accidents and damages. This study focuses on the reliability of Heat Recovery Steam Generator (HRSG) boilers in combined cycle power plants. These plants optimize electricity generation by redirecting gases from burning fossil fuels into heat recovery steam generators. HRSG boiler reliability is pivotal due to high construction costs and the potential for extensive downtime and expenses in case of malfunctions. Addressing this challenge, the research employs the underutilized Reliability Block Diagram (RBD) model, providing a graphical representation of system components and interactions. Specifically tailored to the needs of the Mapna Boiler Company, the study aims to assess and optimize the reliability of the steam production unit, i.e., the boiler, within combined cycle power plants. Research BackgroundReliability, in conjunction with factors such as availability and safety, stands as a cornerstone in ensuring the practical quality of any system. The application of Reliability Block Diagrams (RBD) is a well-established method for modeling and calculating the reliability of industrial systems. Numerous studies have applied RBDs across diverse domains, ranging from power substation automation and wind turbine reliability to error calculations in intelligent submarine power systems. However, despite the versatility of RBDs, a noticeable gap exists in the literature regarding their use for modeling boiler reliability, especially as a multi-state system. Research MethodologyTo undertake a comprehensive reliability analysis of HRSG boilers, the study focuses on distinct subsystems, including:Feed-Water Storage SystemFeed-Water System (FWS)High-Pressure (HP) SectionLow-Pressure (LP) SectionCondensate SystemChemical Dosing System.The Feed-Water System (FWS) is crucial for immediate boiler operation. The initial design involves a Four-Pump System (A2 design) for the FWS. A modification is proposed, removing one feed-water pump, prompting an examination of its impact on boiler reliability. Critical components are identified based on their role in potential disruptions, emphasizing parts causing immediate boiler shutdowns. Using expert knowledge and diagrams, a Reliability Block Diagram (RBD) is developed, visually highlighting weak points. The RBD assesses FWS reliability, comparing two configurations for optimization. Calculation of HRSG Boiler Reliability as a Multistate SystemConfigurations of three-pump and four-pump setups for the Feed-Water System (FWS) are illustrated and analyzed using the Reliability Block Diagram (RBD). The reliability analysis entails a detailed process of data gathering, failure rate determination, and overall reliability calculation for diverse system configurations. The study incorporates probabilities for various operational states and introduces mathematical formulations to calculate the Mean Time Between Failures (MTBF) for water feed system configurations.Fig1: Configuration of HRSG boiler water supply system in 3 pump modeFig2: Configuration of HRSG boiler water supply system in 4 pump mode Steps of optimizing the operational reliability (OPR)Step 1: Identifying Components Used in FWSStep 2: Determining Failure Rates for Each ComponentStep 3: Drawing a Reliability Block Diagram (RBD)Step 4: Evaluating Component ReliabilityStep 5: Calculating Overall Reliability for Each Configuration. ResultsTables present Mean Time Between Failures (MTBF) for water feed system configurations, offering insights into the trade-offs between complete shutdowns and demi-capacity operations. The analysis suggests that the four-pump configuration, while experiencing fewer complete shutdowns, operates at half capacity more frequently compared to the three-pump configuration. The data-driven results highlight the nuances of system reliability and its dynamic nature. Research FindingsThe reliability assessment for boiler construction, considering the failure rates of components over a one-year period, indicates that the four-pump configuration is superior when component reliability is high; otherwise, the three-pump configuration may have an advantage. However, the decision to choose between these configurations necessitates an economic evaluation, accounting for construction costs, shutdown expenses, and half-capacity operation costs. The study underscores the importance of integrating economic considerations with reliability assessments for informed decision-making. Discussion and ConclusionThis research offers valuable insights into vulnerable areas of the HRSG boiler water feeding system, guiding maintenance attention and informing decision-making processes. The study emphasizes the need for future research to consider repair times and incorporate fuzzy reliability values to enhance the robustness of reliability calculations. The holistic approach adopted in this study, combining technical assessments with economic considerations, lays the groundwork for a more comprehensive understanding of system reliability in industrial settings. Suggestions for Future ResearchAs industries evolve, future research should tailor reliability models to specific contexts. Exploring different failure distribution functions beyond the constant-rate assumption opens avenues for investigation. Models like the Weibull mixture model, competitive risk models, compound models, and hybrid models offer promising directions. For instance, the study proposes exploring the application of a compound renewal model, known as complementary risk, for systems with parallel performance and independent components. The limited exploration of this model in the literature presents an opportunity for future research to uncover its potential applications and contributions to reliability modeling.

This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.IntroductionHospital waste encompasses a broad spectrum of both hazardous and non-hazardous materials. The management of hospital waste involves the development of a suitable supply chain network for handling waste generated in the healthcare sector. Improper disposal or mishandling of contaminated waste not only contributes to environmental pollution but also poses a risk of transferring viral pathogens to healthcare and recycling personnel. Research has shown that inadequate disposal of medical waste can lead to the transmission of up to 30% of hepatitis B, 1-3% of hepatitis C, and 0.3% of HIV infections from patients to healthcare workers. This paper aims to design a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic while considering the dimensions of sustainability.Literatur ReviewIn recent years, various studies have delved into the complexities of medical and hospital waste management, proposing mathematical models to address this intricate issue. The current study is built upon the work of Valizadeh et al. (2021). In their paper, a hybrid mathematical modeling approach was introduced, featuring a Bi-level programming model specifically tailored for infectious waste management during the COVID-19 pandemic. The outcomes revealed that, at the higher level of the model, governmental decisions aiming to minimize total costs associated with infectious waste management were crucial. This involved the conversion of collected infectious waste into energy, with the generated revenue being reinvested back into the system. The findings indicated that, through energy production from waste during the COVID-19 pandemic, approximately 34% of the total costs related to waste collection and transportation could be offset. The uniqueness of this study lies in its consideration of three sustainability dimensions: risk, vehicle routing, energy production, employment, and emission of polluting gases. Consequently, the novelty of this research, when compared to previous studies and the article by Valizadeh et al. (2021), is evident in several aspects. It introduces an integrated multi-objective positioning-routing model for the supply chain of waste management under pandemic conditions, taking into account sustainability dimensions, notably the economic aspect, and employs meta-heuristic algorithms for model resolution.MethdologyTo ensure the proper management of hospital waste, the waste is categorized into two groups: infectious and non-infectious waste. It is assumed that waste in hospitals and health centers is segregated and placed in infectious and non-infectious waste bins. The collected waste undergoes further processing: infectious waste is transported to incineration centers, where it is burned and converted into electrical energy, while non-infectious waste is sent to waste recycling centers, where it is reprocessed and returned to the production cycle in the industry. A multi-objective mathematical model is presented to integrate location-routing decisions in the supply chain of hospital waste management, with the following modeling assumptions:Waste segregation at the source helps prevent all waste from becoming viral, reducing the spread of viruses through waste.The risk of spreading viruses is assumed to be relatively equal for each type of waste.Two types of vehicles are considered for transporting waste: the first type carries non-infectious waste, while the second type carries infectious waste.The number of cars, waste collectors, and the capacity of waste incinerators are considered constant in this study.The mathematical model is multi-objective, with the objectives being to optimize the three dimensions of sustainability (economic, social, and environmental).The economic goal is to minimize system costs, including the cost of site location, recycling, collection, segregation of non-infectious waste, and incineration.The environmental goal is to minimize the emission of pollutants in the transportation and processing system in various facilities, as well as to maximize the production of electrical energy.The social goal is to minimize the risk of virus transmission and maximize the employment rate.Results and DiscussionThis research presents a multi-objective mathematical model for the reverse supply chain of hospital waste management during the COVID-19 pandemic in Iran and solves it. The pandemic period is considered a time of maximum utilization of health centers and waste disposal. In this context, a three-objective mathematical model was initially introduced. To solve the model, the krill herd optimization algorithm was employed. The performance of the krill herd optimization algorithm was scientifically and practically evaluated by comparing it with the well-known NSGA-II algorithm. After designing the model, both the multi-objective krill herd algorithm based on Pareto Archive and the NSGA-II algorithm were utilized to solve the model. The results of solving the model demonstrated that the proposed krill herd algorithm, designed in combination with VNS, effectively solved the model and determined the optimal solution within a boundary. Comparing the results of this algorithm with those obtained by the renowned NSGA-II algorithm revealed that the krill herd algorithm produced solutions of much higher quality.ConclusionThe comparison of the Index of dispersion between the two algorithms indicates that the krill herd optimization algorithm explores more points in the solution space, leading to a lower probability of getting stuck in local optima compared to the NSGA-II algorithm. On the other hand, the index of uniformity for the NSGA-II algorithm is lower than that of the krill herd algorithm (lower values are better), suggesting that the multi-objective genetic algorithm explores the solution space more uniformly. Considering the execution time of the two algorithms, it was observed that the NSGA-II algorithm solved the model in less time. Additionally, the increasing trend of execution time in both algorithms confirms the NP-HARD nature of the hospital waste management problem. According to the output of the MATLAB software, considering the presented model, the results affirm the capability to optimally select hospital waste recycling centers.

In a competitive world, one of the most crucial ways to enhance the supply chain performance of manufacturing companies is through integrated scheduling of production and distribution activities. Two significant concerns for dentists and patients include delayed denture deliveries and the multiple production and correction processes for dentures. This research addresses these concerns by developing a mixed-integer linear programming model for solving the integrated production and distribution scheduling problem in a fixed denture supply chain operating under an additive manufacturing environment. The objective functions of this model aim to minimize the cost of production and distribution orders while reducing weighted delays. The Augmented Epsilon Constraint Method is employed to identify Pareto-optimal solutions. To validate the mathematical model, a numerical example and a case study are presented, and various sensitivity analyses are conducted on key model parameters. The numerical results demonstrate substantial improvements in total costs and customer satisfaction levels.IntroductionA supply chain (SC) comprises several interconnected echelons and processes, where an integrated perspective can lead to optimal overall SC performance. Simply improving an organization's internal processes is insufficient for competitiveness in the market; establishing effective relationships with suppliers, distributors, and other SC stakeholders is essential. Achieving maximum value along the SC involves focusing on cost reduction through cost-effective decision-making. In the past decade, the rising adoption of 3-D printing and additive manufacturing technologies in SCs, as a prominent disruptive technology in the Industry 4.0 era, has created numerous opportunities for improving manufacturing SCs compared to traditional production methods. These opportunities include reduced setup and production times, lower safety stock levels, and fewer processing steps. Additive manufacturing has found applications in various fields, particularly in denture production. This research addresses two primary concerns in the field: timely denture delivery and the multiple production and correction processes associated with dentures. A novel mathematical model is developed to tackle these issues, aiming to solve the integrated production and distribution scheduling problem in a fixed denture supply chain operating within an additive manufacturing environment. The objective functions of this model aim to minimize the costs associated with production and order distribution while minimizing the weighted total delays.Materials and MethodsA mixed-integer linear programming model is devised to address the problem outlined in this paper. The Augmented Epsilon Constraint Method is applied to identify Pareto-optimal solutions. To validate the mathematical model, a numerical example and a case study are presented, and several sensitivity analyses are conducted on key model parameters to elucidate their critical roles in the final solutions.Discussion and ResultsA case study is provided to demonstrate the practical applicability of the developed model. Sensitivity analyses on demand data highlight the substantial impact of demand management on final solutions. This research presents a two-objective optimization model to address the simultaneous scheduling of production and order delivery in a three-tier dental prosthesis supply chain. The first tier comprises a dental prosthesis production laboratory, while the second and third tiers include distributors and dentists (final customers). The objective functions include the minimization of total order delivery costs and the average weighted lateness of delivered products from a fixed dental prosthesis production laboratory. Constraints encompass delivery time delays, order allocation to customers, capacity limitations, calculations of time to reach each customer, and vehicle routing. Given that this research problem falls into the category of multi-objective problems, the Augmented Epsilon Constraint Method is employed to obtain Pareto-optimal solutions. To investigate and implement the proposed model, a fixed dental prosthesis production laboratory in Neka City is examined. The numerical results indicate the existence of a trade-off between the problem's objectives.ConclusionsThis paper presents a bi-objective model to address the integrated production and distribution scheduling problem in a three-tier dentures supply chain, aiming to minimize total delivery costs and the average weighted tardiness. The first tier includes a dentures production laboratory, while the second and third tiers comprise distributors and dentists, respectively. Numerical results based on a real case study demonstrate the practical applicability of the model. Several avenues for future research include considering uncertainty in input data and developing efficient meta-heuristic algorithms for solving large-scale instances.

It is inevitable for a manager to consider the performance effects of each component of a multi-stage financial equity capital. These components serve as inputs in the first stage to raise investments. The investments, as outputs of the first stage, become inputs for the second stage and are used in bank services, such as bank facilities, which are outputs of the second stage. Therefore, when evaluating bank performance, the connectivity between the stages must be considered; otherwise, efficiency may not be calculated correctly. Traditional methods often assess multi-stage systems as black boxes, neglecting the potential connectivity that may exist among the stages. We delve into the system and propose models to improve overall efficiency and the efficiency of each stage. Additionally, the continuity and relationships among stages introduce numerous variables and constraints to linear programming for evaluating the entire system. A centralized approach calculates the efficiency score of units simultaneously by solving only one linear programming problem, significantly reducing computational complexity. This approach, especially in large organizations, is commonly employed by central managers. In this paper, we introduce a centralized method for evaluating units with a multi-stage structure. We apply the proposed models to evaluate the efficiencies of bank branches and insurance companies, demonstrating the superiority of the improved network approach and centralized method in enhancing overall system efficiency. Bank branches typically have a two-stage structure, involving labor, physical capital, and other factors.IntroductionBank branches operate under the supervision of a central management team. The central manager, acting as the decision-maker, allocates resources such as labor and financial equity capital as inputs for these branches. The goal is to optimize the overall efficiency of the branches by minimizing the total consumption of resources while maximizing the desired outputs, such as security investments. A common approach to enhancing the performance of banks involves evaluating each branch separately. However, this method does not guarantee the minimization of total resource consumption and can be time-consuming. Since all bank branches are under the control of central management, the decision-maker can optimize the efficiency scores of branches by allocating resources to them simultaneously. This approach, known as centralized Data Envelopment Analysis (DEA), is particularly relevant when certain variables are controlled by a central authority, such as a Head Office, rather than individual unit managers. DEA is a mathematical programming technique used to assess the performance of homogeneous Decision Making Units (DMUs). However, in cases where DMUs have a network structure, such as banks, where the outputs of one division or sub-process serve as inputs for the next sub-process, traditional DEA models treat two-stage DMUs as black boxes and overlook potential connectivity among the stages. In our approach, we consider the internal activities within the system and propose a non-radial model to optimize multi-stage DMUs by taking into account the connectivity among the stages. Furthermore, in previous network DEA models, constraints related to intermediate activities were treated as inequalities, which, as we will demonstrate in this paper, can lead to contradictions in optimality. We address this issue by carefully considering the connectivity among stages. The presence of connectivity among stages introduces numerous variables and constraints to the corresponding model. This model, when used to measure the overall efficiency scores of all DMUs, would traditionally require solving as many problems as there are DMUs, which can be highly time-consuming. In our paper, we introduce a centralized approach that measures the efficiency scores of multi-stage structure DMUs by solving only one linear programming problem. We have applied these proposed models to evaluate bank branches and insurance companies. This approach provides a more comprehensive and efficient way to assess and improve the performance of multi-stage organizations like banks, taking into account the interconnected nature of their operations.MethodologyWe employ the Data Envelopment Analysis approach to evaluate systems with a multi-stage structure, often referred to as a network structure. Traditional DEA models treat two-stage DMUs as black boxes and overlook the potential for connectivity among these stages. In contrast, we delve into the internal activities of the system and propose a model that optimizes multi-stage DMUs by considering the interconnections among the stages. Moreover, in previous models designed to assess network systems, constraints related to intermediate activities were typically treated as inequalities, which could lead to inconsistencies in optimization. In our approach, we enhance these constraints associated with intermediate activities to ensure more robust optimization. Additionally, we apply a centralized approach to allocate resources to DMUs, allowing for the simultaneous optimization of the efficiency scores of all DMUs through the solution of a single linear programming problem. This centralized method streamlines resource allocation and improves the overall efficiency of the DMUs.ResultsWe evaluated 20 bank branches, treating them as 20 DMUs with a two-stage structure. In the first stage, inputs included paid interest, personnel costs, paid interest related to foreign currency transactions, and personnel costs related to foreign currency transactions. The first stage produced intermediate outputs in the form of raised funds and raised funds related to foreign currency transactions. In the second stage, the outputs consisted of loans and common incomes. Notably, some loans in the second stage might become non-performing, where borrowers are unable to make full or even partial repayments. To address this, we considered non-performing loans as undesirable or bad outputs and transformed them into inverse values to treat them as good outputs. To calculate the efficiency scores of the bank branches, we employed both our improved network model and the traditional DEA approach. Our network-based method revealed that many of the bank branches under evaluation were inefficient, in contrast to the traditional method, which inaccurately identified many of the bank branches as efficient. Subsequently, we extended our network method to a centralized case, significantly reducing computation time. The network-based assessment of bank branches took nearly 5 seconds, whereas solving the centralized model required only 0.1 second. In addition to evaluating bank branches, we applied our methods to assess insurance companies. The results demonstrated that our model provided more accurate efficiency scores compared to previous network-based approaches.ConclusionIn multi-stage production systems, the production process comprises several stages. Banks, for example, operate with a network structure in which labor, physical capital, and financial equity capital serve as inputs in the first stage to generate deposits as intermediate outputs. In the second stage, these banks utilize the deposits obtained from the first stage to create loans and security investments. We have introduced models to assess the efficiency of each stage, whether it's the first, intermediate, or final stage, individually. Additionally, we have developed a non-radial SBM model designed for evaluating DMUs with multi-stage structures. The Centralized DEA approach is a valuable method for central managers, particularly in large organizations like bank branches, to allocate resources effectively. We have extended our network-based method to a centralized approach, allowing us to calculate efficiency scores by solving just one linear programming problem. The results obtained from applying our proposed models to evaluate bank branches and insurance companies, both exhibiting network structures as DMUs, demonstrate the superiority of the network centralized approach over previous models.

The closed-loop supply chain is used in practice for several reasons. Firstly, returning products to the production cycle is important for its operation. Secondly, sustainability in the supply chain is a topic of interest for researchers, as the environmental impacts of industries are significant. In this paper, a multi-objective integer fuzzy mathematical programming model is presented to design a sustainable closed-loop supply chain under uncertain conditions. The proposed model aims to maximize profit and social impacts, while minimizing gas emissions into the environment. Since decision makers face uncertainty and doubt, trapezoidal intuitionistic fuzzy numbers are employed to determine the parameter values in the model. To convert the objective functions and model constraints into crisp forms, the expected value and the intuitionistic credibility measure are developed for the objectives and constraints, respectively. Finally, an interactive fuzzy programming approach is utilized to solve the crisp multi-objective problem. Three numerical examples are designed and solved to validate the model and assess the efficiency of the proposed solution method.IntroductionSupply chain management encompasses techniques aimed at coordinating all aspects of the supply chain, from raw material procurement to product delivery or recovery, with the objective of minimizing total costs while addressing conflicts among chain partners. Once raw materials have traversed the forward chain and been transformed into products or services, they may require repair, transformation, or proper disposal, which occurs within the reverse chain. The integration of forward and reverse supply chain methods gives rise to a closed-loop supply chain.Today, one of the primary concerns for organizational managers in supply chain network design is the presence of uncertainties, such as disruptions and uncertain input parameters. Uncertainties can have adverse effects on supply chain performance and decision-making at various network levels, including tactical, strategic, and operational decisions. As probabilistic planning necessitates historical data, which may not always be available or accurate, the theory of fuzzy sets can serve as a suitable option for expressing ambiguity and lack of certainty in parameters. In recent years, environmental factors have received increasing attention. There has been a growing recognition of the importance of environmental effects and the need to incorporate these effects alongside traditional indicators in supply chain design. Environmental considerations are crucial not only for compliance with government regulations but also for improving the organization's social standing from the customers' perspective. Moreover, with the rise of global warming and the accumulation of waste (both renewable and non-renewable, as well as electronic waste and ozone-depleting gases), the importance of managing and controlling these factors has become even more prominent. Despite the significance of environmental issues, there remains a noticeable gap in the supply chain literature concerning the provision of mathematical models based on real-world conditions and efficient solution methods for this problem. This paper focuses on the design of a sustainable closed-loop logistics network that aims to maximize profitability and social factors while minimizing environmental factors. The proposed integrated network considers multi-product and multi-state customer demand under conditions of uncertainty. The significance of this research lies in simultaneously addressing economic, environmental, and social considerations in the modeling process, as previous studies have mostly focused on single or dual objectives. Another innovative aspect of this article is the consideration of parameters in the form of intuitive fuzzy numbers for the design of a sustainable supply chain network.Materials and MethodsIn this research, a comprehensive model addressing the problem of sustainable closed-loop supply chain under intuitionistic fuzzy uncertainty is selected through library studies and internet research. Subsequently, the model is transformed into a deterministic multi-objective model utilizing the intuitionistic credibility measure. Recognizing that decision makers face not only uncertainty but also doubts, trapezoidal intuitionistic fuzzy numbers are employed to determine parameter values within the proposed model. To convert the objective functions and model constraints into their crisp equivalents, the expected value and intuitionistic credibility measure are respectively developed for the objective functions and constraints.FindingsBased on the selected confidence levels and numerical examples, the following observations can be made: In numerical example 1, the first objective function demonstrated that the ABS, SO, and TH methods performed best, respectively. However, in the second objective function, the order shifted to SO, ABS, and TH. Interestingly, all three methods performed equally in the third objective function. The performance of the solution methods in numerical example 2 mirrored that of numerical example 1. Moving on to numerical example 3, the first objective function indicated that the SO, TH, and ABS methods were the most effective, respectively. The order remained similar in the second objective function, and once again, all three methods performed equally in the third objective function. These results indicate the relative superiority of the SO solution method compared to the other methods employed. Additionally, concerning the execution time of the solution methods, numerical examples 1 and 2 exhibited nearly equal execution times for the methods. However, in numerical example 3, the SO, TH, and ABS methods displayed the best performance in terms of execution time, respectively. These findings further emphasize the relative superiority of the SO solution method compared to the others in terms of execution time. It is worth noting that the execution time of each method alone increases significantly with the problem's dimensions across all numerical examples.ConclusionsThis paper presents a multi-objective fuzzy optimization model for the design problem of a sustainable closed-loop supply chain. The model takes into account the concept of sustainability and aims to maximize the income and minimize the costs of the entire supply chain, while also minimizing environmental effects and maximizing social effects. The parameters are considered uncertain and are represented by intuitionistic trapezoidal fuzzy numbers. To handle this uncertainty, the model is transformed into a deterministic multi-objective optimization model using the expected value definition and a chance constraint based on the size of intuitionistic. The obtained deterministic multi-objective model is then solved using the interactive fuzzy mathematical programming method.

One of the major challenges in the automotive industry is facing different risks, especially when introducing new products to meet customer needs. This often leads to difficulties in accurately identifying and adapting to changing methods, designs, new machinery and materials, demand fluctuations, production speed, and more. These factors can result in serious injuries and risks. In order to address these risks, it is crucial to employ effective risk identification methods and prioritize them to exert control over critical risks. Therefore, this paper focuses on identifying the main areas of risks in the automotive industry, specifically within the production line. The identified risks are then categorized and graded. Based on this assessment, a fuzzy cognitive maps approach is developed to analyze 13 risks, which are further divided into three groups: technical, strategic, and operational risks. Furthermore, an interpretive structural modeling approach is used to evaluate the interrelationships among these risks, allowing for a comprehensive understanding of their correlations. Through the network analysis process, the most significant risks are identified. The findings reveal that design errors, low motivation, lack of financial resources, lack of parts, and low productivity are among the top five risks in the ISACO auto parts supply chain. IntroductionThe increasing complexity of industrial systems and the incorporation of new technologies, processes, machinery, and materials have highlighted the importance of considering environmental and safety aspects in risk assessment. Evaluating the impact of failures and their effects is a critical task in industries, particularly in the automotive sector. Among the various risk assessment techniques, failure mode and effects analysis (FMEA) has been widely recognized as a reliable method. Despite the extensive application of FMEA, there are limitations associated with this approach. One of the significant drawbacks is that it considers the SOD factors independently without considering the interdependencies among failures. In reality, production stages are not executed simultaneously, and potential failures do not occur concurrently. Some failures are influenced by previous stage failures and, in turn, affect subsequent stages. On the other hand, interpretive structural modeling (ISM) allows for the comprehensive structuring of a set of interconnected factors in an organized model. By utilizing fundamental concepts of graph theory, ISM describes the intricate pattern of conceptual relationships among variables. In this way, it overcomes the limitations of independent consideration of failures in FMEA. Therefore, this paper employs ISM as an approach to assess the impact of failures. It provides a comprehensive and structured model that captures the interrelationships among various factors. By using this approach, the evaluation of failures becomes more accurate and reliable, considering the interdependencies among different stages and failures.Materials and MethodsThis research is categorized as applied research in terms of its objective and descriptive-qualitative in terms of its method. Field studies were conducted as the data collection tools for this research. The scoring method (utilizing experts) was used for data analysis, and a case study of the ISACO company was employed to test the model. The required data for this research, aimed at presenting a model for identifying production risks in the first stage, were collected through a literature review. Relevant English and Persian books, student theses, related websites, journal articles, conferences, and seminars focusing on the identification of multi-stage production risks were used to gather research literature. Existing documentation from various industries was also utilized in the field of risk assessment and identification. In the initial stage, the main risks of the automotive parts supplier company are identified. In this phase, risks identified in existing scientific research sources were finalized through interviews with experts. The extracted risks are evaluated and ranked based on the failure mode and effects analysis method in the second step. In the third step, the interactions among various risks are examined using the fuzzy cognitive map approach. The results obtained from the second step are utilized in this phase through normalization. In the fourth step, the final ranking of risks is determined based on the static analysis conducted in the third step. In the fifth step, an interpretive structural model is used to determine the interdependence and susceptibility of risks to each other.Discussion and ResultsBased on the research objectives, the risks in the production line domain were first identified using the FMEA (Failure Mode and Effects Analysis) approach. Then, the FCM (Fuzzy Cognitive Mapping) method was employed to design a fuzzy network, and ultimately, the ISM (Interpretive Structural Modeling) approach was used to analyze the penetration and interdependence of risks. The ranking of risks using the FMEA approach is as follows: lack of motivation, parts shortage, low productivity, rework in execution, and weak supervision are ranked from 1 to 5, respectively. After considering the interactions among risks in the dynamic analysis of FCM, the factor of lack of motivation descends from rank 1 to 7. Furthermore, the factors of low productivity and lack of financial resources rank first and second, respectively.ConclusionDecision-making in the field of risk management involves considering various factors that are subject to change over time. The dynamic nature of these factors can influence the effectiveness of risk management decisions, and their impact on the desired outcomes needs to be carefully assessed. Proper risk management requires a comprehensive understanding of potential failures and the ability to predict and mitigate their consequences. Analyzing risks, employing effective mitigation strategies, and conducting thorough evaluations are essential for ensuring the success of a project or business venture. Professional risk management involves identifying and addressing potential vulnerabilities, evaluating their impact on the desired objectives, and devising appropriate strategies to prevent or mitigate their occurrence. The use of risk assessment methodologies, such as Failure Mode and Effects Analysis (FMEA), allows for systematic identification and prediction of potential failures, while incorporating flexibility and adaptability in risk mitigation approaches. These methodologies offer advantages such as scalability, speed, high accuracy in predicting failures, enhanced understanding of complex systems, and facilitation of decision-making processes. By employing fuzzy cognitive mapping (FCM) in FMEA, the prioritization and prediction of potential risks can be effectively performed. This approach provides a more flexible and comprehensive understanding of risks, enabling easier decision-making and utilization of valuable feedback from domain experts. Following the identification of primary risk areas, the risks associated with production lines were classified, and a fuzzy cognitive mapping approach was developed based on this classification. Thirteen identified risks were then analyzed using interpretive structural modeling (ISM) to assess the interrelationships among the risks and provide further insights for decision-making.

Estimating the revenue efficiency of entities being evaluated is crucial as it provides valuable information about organizations, assuming that the output prices are known. This research introduces a new definition of optimal scale size (OSS) based on maximizing the average revenue efficiency (ARE). Additionally, the ARE is defined for both convex and non-convex sets, independent of returns to scale and the assumption that the vector of input-output prices of units is uniform. Moreover, to address the presence of uncertain data in real-world applications, the introduced ARE model is extended to evaluate systems with random inputs and outputs, along with approaches for its calculation. Finally, the proposed method is applied in an experimental example, calculating the ARE for a dataset of postal areas in Iran.IntroductionThe concept of optimal scale size has been extensively studied in the field of data envelopment analysis. Cesaroni and Giovannola's research on non-convex FDH technology reveals that the optimal scale size is a point in the production possibility set that minimizes average cost efficiency. Average cost efficiency, a new measure combining scale and allocation efficiencies, provides a more accurate performance assessment compared to cost and scale efficiencies. When evaluating units with known output prices instead of input prices, assessing revenue efficiency can offer more valuable insights. This paper extends the research on cost evaluation to revenue evaluation. It introduces the concepts of average revenue efficiency and optimal scale size based on revenue maximization. The optimal scale size based on revenue maximization is defined as the point in the production possibility set that maximizes the average radial income for the unit under investigation. Average revenue efficiency serves as an evaluation measure of unit revenue, surpassing revenue and scale efficiencies in accuracy. The paper examines methods for calculating average revenue efficiency in both convex and non-convex technologies. It demonstrates that the average revenue efficiency model in convex technology with variable returns to scale is equivalent to the revenue model with constant returns to scale. Furthermore, the relationship between optimal scale size points based on revenue maximization and the most productive scale size is determined. Next, the paper presents the average revenue efficiency model for stochastic sets with the presence of stochastic data. An experimental example is used to calculate the average revenue efficiency and obtain the optimal scale size for a set of postal areas in Iran.Materials and MethodsThe study builds upon Cesaroni and Giovannola's method for calculating average cost efficiency and optimal scale size to develop models for average revenue efficiency and optimal scale size based on revenue. It also utilizes chance-constrained probabilistic models with a deterministic objective function in DEA literature to present average revenue efficiency for stochastic sets. The model is transformed from stochastic to deterministic and then converted into a linear model using the error structure method.Discussion and ResultsThis paper introduces average revenue efficiency and optimal revenue scale size, demonstrating the equivalence between the average revenue efficiency models in convex technology with variable returns to scale and those with constant returns to scale. It also presents the average revenue efficiency model for stochastic sets, enabling the calculation of average revenue efficiency and optimal revenue scale size for units with random inputs and outputs.ConclusionIn many real-world scenarios, particularly when output prices are known, evaluating revenue efficiency holds greater significance than cost efficiency. This study develops the concepts of average cost efficiency and optimal scale size for revenue evaluation, expanding upon the existing literature on data envelopment analysis. The paper demonstrates how average revenue efficiency can be calculated as a valuable and accurate measure of efficiency in convex and non-convex technologies, without making assumptions about returns to scale. By assuming the randomness of input and output variables and employing chance-constrained models, a quadratic deterministic model is presented to calculate average revenue efficiency. It is then transformed into a linear model assuming uncorrelated variables, enabling the determination of average revenue efficiency and optimal scale size based on revenue maximization for random units. The proposed models are applied to a real-world sample, evaluating the average revenue efficiency of twelve postal units. The results highlight the models' ability to provide a more accurate evaluation of revenue efficiency and identify the best revenue scale size as the reference for inefficient units.

Data Envelopment Analysis (DEA) is an approach based on mathematical programming for the relative evaluation of decision-making units treated as similar yet distinct production systems. In this approach, the performance of each unit is characterized by describing the transformation of specific inputs into specific outputs. Traditional DEA models assume that the role of each performance factor is clearly defined. However, in some real-world problems, certain factors might be identified as dual-role factors depending on the evaluation nature or the decision-makers' perspective. These factors can play the role of both input and output, or even be considered neutral in assessing the units' performance. In the current paper, to determine the status of dual-role factors and calculate the efficiency of DMUs, two new linear programming models, based on the concept of deviation in the efficiency constraint and a common set of weights, are suggested. The main advantages of the proposed models are significantly reducing the computations and iterations required to solve the model, and involving all DMUs to determine the role of factors. To assess the performance of the proposed models, a data set for the evaluation of eighteen suppliers in the presence of two inputs, three outputs, and two dual-role factors has been employed. The obtained results showed that, compared to other models, the proposed models are computationally more efficient, and the role determination and evaluation of the units, based on the obtained weights from these models, are better aligned with the expectations of decision-makers

In general, numerous studies have paid a special attention to machine planning, job allocating and job sequencing in scheduling problems to optimize makespan. Due to the relation among economy, energy and environmental concerns, energy use is one of the most important issues in different systems planning. In this paper, a scheduling of heterogeneous parallel machines is studied, in which the job process speed on every machine is settable. Since there is a direct link between used energy of machines and process speed, the purpose of the paper is to minimize total used energy and tardiness-related costs in delivering customers' demand. In order to optimizing the problem, two meta-heuristic algorithms, Memetic algorithm and Genetic algorithm, are developed, finally the results of both algorithms are analyzed and then compared to each other as well as to the results of the GAMS optimization software.

This paper deals with the problem of two-stage assembly flow shop scheduling with considering sequence-independent setup times .The objective is to minimize totalcompletion times of all orders. In this problem, there are several orders for one type of product. Each ordered product is formed of several different parts. At first, the parts are manufactured in a flow shop stage with some different machines and then they are assembled into a final product on a single machine. This paper presents three meta-heuristic algorithms, namely Parallel Variable Neighborhood Search (PVN) Artificial Immune Algorithm (AIA) and Simulated Annealing (SA), for solving under studied problem. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of presented algorithms. Also, Numerical experiments are used to evaluate the performance of the proposed algorithms. The results show that the PVNS algorithm performs better than the other algorithms

The goal of the dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. One of the most important aspects of this issue is to consider the transporters and minimizing material handling time. This paper developed a bi-objective mathematical model which is able to simultaneously minimize the material handling costs between facilities and the cost of rearrangement facilities and material handling time. Due to probabilistic characteristic of the transporters, such as the time of handling operation and existence of the failure, calculating the time required to carry the material using analytical relationships is impossible. Therefore, this paper uses the simulation approach and artificial neural networks. In this approach, a lot of scenarios are generated by combining of variables. Each scenario shows the location of the facilities and how to perform transportation operations in each period. Then each of these scenarios is implemented through computer simulation and simulation results are considered as the response variable. Finally, using input and response variable, an artificial neural network is trained so that it can accurately estimate the time of carrying out the transportation operations. Given that the above problem is a NP-hard; this paper proposes a new Multi-Objective meta heuristic algorithm to optimize the problem and compares the performance of the proposed algorithm with existing algorithms in literature.

The aim of dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. This paper developed a bi-objective mathematical model which is able to simultaneously minimize the material handling costs between facilities and the cost of rearrangement facilities and material handling time. Due to probabilistic characteristic of the transporters, such as the time of handling operation and existence of the failure, calculating the time required to carry the material using analytical relationships is impossible.Therefore, this paper uses the simulation approach and artificial neural networks. In this approach, a lot of scenarios are generated by combining of various levels of variables. Each scenario shows the location of the facilities and how transportation operations in each period is performed. Then each of these scenarios is implemented through computer simulation and simulation results are considered as the response variable. Finally, using input and response variable, an artificial neural network is trained to accurately estimate the time of carrying out the transportation operations. Given that the above problem is a NP-hard; this paper proposes a new meta-heuristic algorithm to optimize the problem and compares the performance of the proposed algorithm with existing algorithms in literature.

One of the important decisions in the supply chain is the supplier selection affecting different sectors and objectives of a chain significantly. When it comes to supplier selection, product/technology selection, product/technology transfer method selection, and product/technology supplier selection are three debatable factors. Hence, in this paper, criteria influencing these three indicators will be identified and their importance will be ranked by Gray-DEMETEL and Best-Worst Method (BWM). Then, the priority of the options will be determined using Gray Analytical Network Process (GANP), which is one of the strongest decision-making methods. This paper attempts to demonstrate the superiority of the BWM method in determining the importance of criteria while comparing to previous methods in this group. The compliance rate obtained for the BWM confirms this claim. In order to investigate the proposed approach, a case study was conducted while considering 5 final suppliers, 3 technologies and 5 technology transfer methods, which ended with the selection of the best supplier with the highest technology and capable of transferring the technology by the optimal transfer method.

In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization model are the reduction of blood supply chain costs along with minimizing the shortage and expiration rate of blood products. The Markov chain has been used to address the uncertainty of donor blood supply. To estimate the needs of medical centers, the received demand is considered fuzzy. Then, the proposed model is solved in small dimensions by GAMS software and in large dimensions by Bat and Whale meta-heuristic algorithms, and the results are presented. In addition, a case study is presented to show the applicability of the proposed model. The results show a reduction in the level of costs as well as a reduction in the shortage and expiration of blood products in the supply chain.IntroductionOne of the important topics researched in the global healthcare systems of different countries is the improvement of supply chain performance. The health system has one of the most complex and challenging supply chains due to its direct relationship with human lives. Issues such as uncertainty in blood demand and supply, blood inventory planning, delivery schedule, ordering time, attention to expiration date, and limited human resources are among the challenging issues in the field of health, especially the supply chain of blood and blood products. A unit of blood, from the time it is received from the donor to the time it is injected into the patient as whole blood or blood product, includes many processes and challenges that must be taken into account to ensure the health of the blood and the health of the supply chain. Redesigning an existing blood supply chain is not possible in the short term due to significant costs and time required, so using existing facilities and optimizing conditions is more preferable than reestablishing equipment, blood centers, and other facilities related to the blood supply chain. In this research, by presenting a mathematical model, we try to optimize the tools and facilities in a blood supply chain. The important goal in the blood supply chain is the cost factor. The costs incurred on the blood supply chain include costs such as blood collection from volunteers, product processing and blood inventory costs in hospitals and blood centers, and blood transfer costs to demand centers. On the other hand, the balance in storage and waste reduction is also very important in this chain. High storage increases the amount of inventory (increase in cost) and also increases the rate of perishability (increase in cost) of blood products. It is important to pay attention to the fact that the reduction of costs should be accompanied by the reduction of shortages and waste. In addition to the lack of blood, improper distribution and untimely supply of blood to hospitals can be completely disastrous. Requests to blood centers are made under certain conditions, such that the requested product(s) are separated in terms of blood group or the presence or absence of a specific antigen. Paying attention to blood groups and compatibility indicators is one of the principles of blood transfusion, and not observing them can cause unfortunate events.Due to the disproportionate percentage of distribution of blood groups among volunteers, there has always been a possibility of a shortage in the supply chain. In the medical world, in case of a shortage of a blood product of a certain group, attempts are made to replace that product from groups that can be matched. This will reduce the shortage and save the lives of patients whose blood with the required blood group and RH is not available at the same moment. In order to solve this challenge, in the upcoming research, a solution based on the versatility of unanswered demands will be considered, which will be included in the mathematical model. Another important issue is the age of the demand for the requested product, which creates an age-based demand in the supply chain. (Some special patients need fresh or normal products according to the type of disease.)MethodologyIn this research, a comprehensive mathematical model has been developed in the form of a MINLP model. The research model is based on a comprehensive blood supply chain consisting of three components: collection, processing, and consumption of blood products. There are three types of collection centers in this model: first, vehicles that serve blood donors at predetermined locations and collect blood; second, fixed collection facilities located in some areas of the city that solely perform the task of collecting blood; and third, blood centers (blood transfusion centers) that perform both blood collection work and other tasks related to product processing, testing, and transfer planning to demand centers and hospitals. The next part of the model is related to the processing of the collected blood. In this part, the blood collected by the collectors in the blood center is aggregated, the percentage of each blood group is determined, and according to the need in the blood centers, products such as red blood cells, platelets, and whole blood plasma are sent to hospitals. It is worth noting that as blood is converted into other products, some characteristics of the product, including the age of the products, differ from each other. Therefore, in the continuation of transferring the products and responding to their demand, the age of the blood product will be considered. Additionally, it should be noted that the blood product requested from the demand centers is in two forms. For some special patients and in special surgeries, a series of blood products with a certain age (young blood) are needed. Therefore, the importance of the age of the blood sent to the hospitals is also seen in the model. In the real world, in the face of a shortage in hospitals, a solution is thought out, which is to use the principle of adaptability of blood groups. Through a pre-accepted adaptability matrix, a series of demands for blood groups g, in case of shortage, can be satisfied with the supply of blood groups f turn around. Deterministic supply chain network design models do not take into account the uncertainties and information related to the future affecting the supply chain parameters and as a result cannot guarantee the future performance of the supply chain because due to the inherent and fluctuating and sometimes severe change in the environment of many operating systems Parameters in optimization problems have random and non-deterministic characteristics. In this research, two different approaches have been used to face the uncertainty in blood supply and demand values. For the demand, a triangular fuzzy approach has been proposed. According to the conditions of uncertainty, the appropriate alpha cut is selected based on the opinion of the decision-makers, and the demand is adapted to the conditions. Regarding the amount of supply, in order to estimate the number of donors in future periods, we have used the Markov chain to predict the number of donors based on the records in the past.FindingsIn order to evaluate the presented model, it is necessary to solve the research in both small and large sizes to determine the reaction of the research target function to changes in the parameters of the problem. For this purpose, the research model was first coded in GAMS 24.1 software. According to the designed sample problems, up to a certain size, it is possible to solve the problem within a certain time frame using GAMS software. However, as the size of the problem increases and the time to reach the answer also increases, meta-heuristic algorithms such as WOA and BAT were employed to solve this problem. The results indicate that the Whale Optimization Algorithm (WOA) performed better. Subsequently, based on a case study, a problem was presented to illustrate the efficiency of the model and its solution method. The results obtained for the objective function and the values obtained for the main variables of the research demonstrate the effectiveness of the model and its solution approach.ConclusionThe purpose of this article is to design a comprehensive supply chain that includes three parts: collection, processing, and distribution of blood products. The supply chain comprises mobile and fixed blood collection units that receive blood from donors and send it to blood centers. At these centers, blood is processed into required products and then distributed to demand centers based on demands categorized as fresh or normal products. In this research, the objective was to minimize costs such as blood collection, blood inventory in blood centers and hospitals, as well as the cost of blood products expiring due to non-use. To address blood deficiency, the blood compatibility system was incorporated into the model. This system ensures that if a certain product of a certain group is not available, a compatible product from another group is sent as a replacement. The model was solved using the exact solution approach of GAMS software for smaller-sized problems. However, for larger-sized problems, meta-heuristic algorithms such as WOA and BAT were employed to achieve reasonable solving times. Additionally, a fuzzy coefficient was proposed for relatively accurate demand prediction, and the Markov chain and the Kolmograph left-hand theorem were utilized to predict the number of blood donors. The results obtained from small-sized problems using accurate solver algorithms, as well as medium and large-sized problems using WOA and BAT meta-heuristic algorithms, demonstrate the efficiency of the designed model. Finally, a sensitivity analysis based on changes in fuzzy coefficients of demand and coefficients, including the alpha cut transformation function, and its effect on the objective function are presented.

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency for a set of Decision Making Units (DMUs) based on their inputs and outputs. There are weaknesses in conventional models DEA. Most important of which is the weight shift input and output which makes the efficiency of Decision Making Units with different weights measured. A characteristic of Traditional DEA models is that it allows DMUs to measure their maximum efficiency score with the most favorable weights. As well as the conventional DEA models are not focused network of evaluation units. In this paper we propose to correct the weaknesses the common set of weights (CSW) in network DEA model based on the Goal programming approach. To test the effectiveness of the proposed model and solve real data is used by insurance companies active in Qazvin province. The model presented in this paper units decide on a similar scale with a set of weights for neutral evaluation is common. Proposed approach helps policy makers to better understand the strengths and weaknesses of DMUs and try to promote the strengths and remove weaknesses to improve the efficiency and ranking of given DMUs.

The increasing concern about the deteriorating effects of supply chains related activities on the environment has led to the growing attention to develop green closed-loop supply chains in order to minimize greenhouse gases emission. This paper presents a green closed-loop supply chain model developed under the demand uncertainty aiming at minimizing total cost and total CO2 emission across the supply chain, and maximizing the product’s market share in the presence of a competitor. In this regards, an agent-based market model is developed to estimate the demand’s parameter function then a hybrid simulation model which integrates agent-based and discrete event simulation modelling approaches is designed to simulate the closed-loop supply chain which is the novelty of this paper. Then, scenarios are created using Taguchi design of experiments (DOE) method, and are executed with the market model and the supply chain model to capture total cost, total CO2 and market share. A decision matrix is configured using scenarios and recorded results for three mentioned criteria and ELECTRE and SAW methods are used to rank scenarios and select the best one. The other contribution of this research is its comprehensiveness in considering variables related to three categories of inventory replenishment policy, marketing mix (price and advertisement) and transportation. An automotive industry case is provided to demonstrate the capabilities of the model and its applicability and effectiveness in resolving real-world problems.

Environmental concerns have spurred an interest in studying green supply chain. Nowadays, governmental and non-governmental organizations consider environmental management as a strategic requirement having numerous benefits. Therefore, they effort to increase customers' satisfactory and market share considering external factors like environmental consequences in addition to internal factors. In this paper, a bi-objective mixed integer programming model is developed to identify the optimal location for manufacturers and disassembly sites in a green supply chain network design. This paper addresses the role of the reliability of facilities and vehicles to ensure effective stream among supply chain network, the objective functions are defined as total cost minimization, and total co2 emissions minimization. Besides, uncertainties on the network design are investigated through two-stage stochastic programming. with respect to the fact that the model is non-linear and bi-objective, at first, an approach is presented to linearize it and then the proposed bi-objective mathematical model is solved as a single-objective one by compromise programming method. The effectiveness of the proposed model is demonstrated by using of a numerical example derived from a real case.

In the last decade, despite the extra importance of service and its increasing development in global and national economies, it has received less attention in comparison with manufacturing industries. The evaluation of performance in service organizations has always been one of the main challenges in this area. Parasuraman and colleagues’ five dimensional model is one of the most common models to evaluate the quality of service and was used in this paper to evaluate the service quality in CNG stations in Zanjan Province. In this paper, the Data Envelopment Analysis (DEA) was also used to assess the relative service quality in 36 CNG stations in Zanjan province. Due to the high importance of service quality, it was considered as the functional output of CNG Refueling Stations. The number of employees, number of dispensers, fixed monthly cost, and monthly allocated credit to stations were considered as functional inputs of CNG stations. After obtaining the station’s rank, finally, the sensitivity of results was analyzed using the proposed method of Jahanshahlu and colleagues. Also, the results showed that the developed model of multi criteria data envelopment analysis has better performance than basic models.

In the scheduling problems, it is commonly assumed that processing times are fixed and known. In the literature of project scheduling emphasizes that the time of each activity/operation can be multi-mode and by assigning more resources, the activity time can be reduced. In these problems, in addition to activity scheduling, allocation of available limited resources to the activities should also be carried out. This assumption that processing time of activities is fixed is a weakness in scheduling literature. This paper develops the classic problem flow shop scheduling to multi-mode resource-cosntrainted flow shop scheduling problem. This paper discusses comprehensively about mathematical modeling. In this regard, two mixed integer linear programming models with two differnet concepts are presented. The first model is location-based model and the second is sequence-based. The performance of the models are evaluated by comparing their size and computational complexities. In the size complexity, the first model requires more variables but less constraints than second Model. In the computational complexity, the first model significantly outperforms than the second Model. Also, the first model, besides solving more problems as optimally, requires less time to solve than the second model

Location-Routing problem with Cross-docking is as a New Research Area for Distribution Networks in The supply chains. The purpose of this paper is to simultaneously design a location for cross-docking center and routing vehicles due to the system cost minimization which, is known as an NP-hard problem. This paper presents a two-stage mixed-integer programming (MIP) model for the location-routing multi-commodity problem with cross-docking due to potential applications in the distribution networks. The principal innovation of this paper includes multiple commodities and its solution method as a hybrid algorithm based on the artificial immune system (AIS) and artificial fish swarm (AFS) algorithms. Also, assumptions are given in the proposed model that distinguishes it from the models are presented in this area. Finally, to evaluate the efficiency of the proposed algorithm small and large-scale test problems are randomly generated and the proposed MIP model solved by artificial immune system (AIS) and artificial fish swarm (AFS) and a sample algorithm and then compared with each other. The computational results for different problems show that the proposed hybrid algorithm performs well and converges fast to reasonable solutions

In this paper, the problem of job scheduling and preventive maintenance activities is investigated simultaneously in an unrelated parallel machine’s environment with machine deterioration. Due to the existence of deterioration phenomenon and its effect on the job scheduling, the aim of this paper is to find the ideal time, the optimum number of maintenance activities and the sequence of the jobs, for minimizing the sum of job's completion times. Preventive maintenance activities may be needed because of tool wears or machine deterioration in a job scheduling horizon. Here, two different deterioration time function and tool wear are studied, in which the machines are reverted to the original condition after any preventive maintenance activity. The duration of each activity is considered as a function of its starting time and its position on the sequence of job scheduling. A mathematical model is proposed and due to its complexity, a simulated annealing Mehta-heuristic algorithm is used for solving the model. Finally, numerical examples are given and sensitivity analysis with various parameters is applied to evaluate the performance of the proposed method

Cross-docking is one of the lean logistics tools that is used for uniting the shipments during the loops replacement. Cross-docking is the process of product movement form distribution centers without storage function. Vehicle routing problem in Cross-Dock external environment has much influence on cross-dock costs. This paper provides a model for minimizing total distance traveled by vehicles in the external environment of a cross-dock. In this paper, Vehicles routes was modeled with capacitated vehicle routing problem (CVRP) and genetic algorithm (GA) was used to solve the model. To validate responses obtained by GA, simulated annealing (SA) was used. Also, to evaluate the efficacy of two algorithms (SA & GA) in different CVRP problems in cross-dock, 10 problems with different dimensions are evaluated. The results show that in problems with smaller size GA is more efficient, whereas in large size problems SA is more efficient

Subway network extension, growing expansion an inherent management complexities, made it necessary to use scientific approach, modern technology and global experiences about maintenance. Many equipments and trains of subway network, consist of repairable items which consume considerable human and financial resources of the organization. This paper aims to measure the efficiency and rank ventilation system of Tehran's DC trains and it's subsystems using three-stage relational data envelopment analysis model. About the importance of Tehran’s subway ventilation system, we can say that for 8 months of the year, ventilation system after driving engines which support the movement of trains, gets most important attention of operation management. In this paper with defining ventilation system as decision-making units, we use it to evaluate the system performance and ultimately for maintenance strategy, including an estimate of the maintenance workshop capacity, spare parts and requiring labor. Due to various errors such as human errors, machinery errors, limitations of field data analysis and etc, in this paper fuzzy logic is used to overcome the uncertainly in data. Also, reliability, availability and maintainability analysis, which are most important indicators in the maintenance field, have been used to determine the ventilation system inputs and outputs

This paper focused on locating ATMs problem. At first, this paper aims to identify effective factors in selecting ATMs appropriate locations. After selecting potential locations, optimal locations are determined through mathematical modeling. In this paper, effective factors to determine potential locations are weighted through Fuzzy AHP. Finally, linear allocation technique with multi-objective approach is used to determine ATMs locations at Bushehr city. After literature review, 46 effective factors are identified and then, based on experts' opinion are categorized in 6 groups of demographic, economic - social factors, competitive – commercial factors, public utility (infrastructure and proximity) factors, investment factors and political - legal factors. Results from Fuzzy AHP show that demographic factors are the most important factors among others. Final results show that each of these six categories, can have different effects on decision makers.

By developing information technology and emerging online markets,Planning for these markets and analyzing their details has been became a priority for beneficiary organizations. One of most important online markets in Iran is SIM card credit services that are considered as online stores. Regarding to numerous and increasing number of online malls, grouping and classification of online malls from supplier's point of view in order to forecasting cooperation is essential. In this paper, around three thousand online stores has been studied and analyzed by using one of famous supplier data and it has been clustered according to supplier's indexes. Clustering process has been done by using SOM Neural Network in two levels by K-means algorithm since it facilitates analyzing of clusters. Although various validation indicators has been developed to determine the best number of clusters but in this paper, an optimizing model of compensatory approach to indexes is presented by using combination multi criteria decision making and aggregation of various indexes

In this paper hybrid flowshop scheduling problem where some jobs, not all, have to follow no-wait restriction (that is, the operations of that job must be processed with no stop) is examined. In the literature, all papers assume that all jobs of the shops have to follow no-wait restrictions. First, this paper mathematically formulates the problem with two different mixed integer linear models under proposed considerations. The models are evaluated using two performance measures of size complexity and computational complexity. The small instances of the problem are solved using commercial software of mathematical programming. To solve larger instances of problem, two solution algorithms have been developed. These two algorithms are based on imperialist competitive algorithm and simulated annealing. A comprehensive numerical experiment including small and large instances is conducted to evaluate the models and algorithms. The results show that the imperialist competitive algorithm outperforms simulated annealing

In this paper, multi-site aggregate production planning for the production of perishable products such as gifts of New Year, calendars and maturities by postponement policy in uncertainty conditions is determined. The production process for these products is proposed to be divided into two phases including the production of final and semi-finished products with applying the concept of postponement. So, there are three production activities, including direct production, the production of semi-finished products and final assembly. Also, a robust optimization model to solve aggregate production planning problem for these products is developed. Finally, a set of real data from a calendar producing company in Tehran called “NIK Calendar” are used to validate and show the efficiency of the proposed model. Results show that the proposed model of this paper can use for similar factories which are active in the field of aggregate production planning with considering uncertainty in the parameters

This paper focuses on supply chain coordination under a revenue sharing contract. Demand plays a crucial role in modeling inventory systems, particularly in the retail industry where products need to be brought in and taken out of retail stores within specified timeframes. The question addressed is how to allocate shelf space effectively. Optimal shelf space allocation in retail significantly impacts product sales and profitability. The research demonstrates that by considering factors such as price, allocated shelf space, product brand image, and advertising, a new demand function can be developed. The study explores decentralized, centralized, and coordinated structures using a Stackelberg game model. The findings show that a revenue sharing contract leads to a win-win outcome in the supply chain. Additionally, a numerical example is provided to illustrate the model, and sensitivity analysis is performed on key parameters. The results highlight the significant impact of price elasticity on demand, emphasizing the need to pay close attention to this parameter in real-world applications.IntroductionCoordinating manufacturers and retailers has always been a challenge in decentralized supply chains, as each member seeks individual profit. One practical mechanism for coordination is the revenue-sharing contract, where manufacturers set the wholesale price and retailers pay it. Additionally, retailers share a portion of their profits with manufacturers to incentivize participation in coordinated structures. However, the percentage must be chosen carefully to maximize the profits of both chain members compared to a decentralized structure. In today's competitive world, securing shelf space and determining optimal pricing, particularly in chain stores, has become increasingly intense. Companies can increase their profits by considering various factors that influence customer behavior. This study investigates how manufacturers and retailers in a supply chain leverage advertising and brand image to drive demand while setting prices and allocating shelf space for their products. This new model effectively captures the concept of competition in the market. On the other hand, in today's highly competitive business world, the competition to secure shelf space and determine pricing in department stores has intensified. Pricing and shelf space decisions are influenced by the demand within the supply chain. By considering various factors that impact customer behavior and incorporating them into the model, even though it introduces complexity and challenges in solving the model, the results become more realistic and enhance the model's effectiveness. Customers place great importance on accessing products at the right time and place, making optimal allocation of shelf space in stores and effective shelf space planning crucial steps towards improving and increasing sales in retail stores.Materials and MethodsA two-echelon supply chain, comprising a manufacturer as the leader and a retailer as the follower, is the focus of this study, and Stackelberg game theory is applied to model and analyze this system. The investigated model considers decentralized, centralized, and coordinated structures. Through this research, various factors such as price, allocated shelf space, and the impact of product brand image and advertising are taken into account, leading to the development of a new demand function. The application of the Stackelberg game in the developed model demonstrates how a revenue sharing contract can result in a win-win outcome within the supply chain. Real-world performance analysis of the proposed models is conducted using a dataset from Golrang Holding Supply Chain Network and Ofogh Kourosh Chain Stores Company in Iran.Discussion and ResultsThe proposed problem is modeled under three structures: (1) a decentralized structure where each member of the supply chain (SC) makes independent decisions to optimize its own profit, (2) a centralized structure where a central administrator maximizes the overall SC profit, and (3) a coordinated structure achieved through the design of a revenue-sharing contract. The revenue-sharing contract encourages SC members to transition from the decentralized structure to the centralized one. The results demonstrate that the use of the revenue-sharing contract leads to the sum of retailer and manufacturer profits being equal to the total profit in the centralized structure, with each member achieving higher profit compared to the decentralized structure. Consequently, the revenue-sharing contract facilitates the coordination of the desired supply chain.ConclusionsThis research is based on a real-life case study in the retail industry. The findings of this study are applicable to various retail sectors, including dairy, protein, grocery, cosmetics, fresh fruits, vegetables, and more. Traditionally, price has been viewed as a revenue generation tool. However, it is now recognized that price plays a crucial role not only in generating revenue but also in ensuring customer satisfaction. Therefore, it is important to coordinate and align pricing decisions with factors related to customer management, such as brand image and advertising. The main objective of this research is to design a new model with a multiplicative demand function that considers factors such as price, shelf space, brand image, and advertising. Additionally, the implementation of a revenue-sharing contract has improved system performance. The developed models were solved using Mathematica software, and numerical examples were provided to demonstrate their real-world application and the solution method. The numerical examples revealed that the centralized and coordinated structures experienced price declines compared to the decentralized model, leading to increased profitability in these structures. Furthermore, sensitivity analysis was conducted on key model parameters, highlighting the significance of the price elasticity parameter on demand. This parameter should be given greater consideration in real-world applications. While the study focused on a supply chain structure involving one manufacturer and one retailer, future research can explore supply chains with multiple retailers. It is also possible to combine various supply chain coordination contracts or compare different coordination contracts with each other.

This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.IntroductionA production line consists of a sequence of workstations, in each of which parts are processed by machines. In this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. In production lines, the buffer capacity and processing time of machinery have a significant impact on the system's performance. The presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. Previous research has investigated production lines without any possibility of machinery failure, referred to as "safe production lines." However, in real production lines, machinery failure is inevitable. Therefore, several studies have focused on "uncertain production lines,"assuming the existence of a probability of failure in a deterministic or exponential distribution. This research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. The objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. The approach aims to maximize the system's output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. Moreover, simulation can be applied without interrupting the production line or consuming significant resources. In this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system's output rate. Therefore, the simulation technique has been utilized to optimize the production line.Research methodThe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. In the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. To adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line. To achieve this, the desired production system is simulated. The design of experiments is used to generate scenarios for implementation in the simulated model, and the response surface methodology is utilized to analyze the relationship between the input variables (such as the average processing time of each product type in each workstation and the buffer volume between stations) and the response variable (production rate).ResultsTo implement the proposed methodology based on the designed multi-objective programming model, a case study of a three-product production line with 9 workstations and 8 buffers was conducted. Subsequently, to compare the performance of the optimization algorithms, five indicators were used: distance from the ideal solution, maximum dispersion, access rate, spacing, and time. For this purpose, 30 random problems, similar to the mathematical model of the case study, were generated and solved. Based on the results obtained, both algorithms exhibited similar performance in all indices, except for the maximum dispersion index.ConclusionsIn this article, the structure of a multi-objective mathematical model was sought in uncertain multi-product production lines with the combined arrangement of machines in series-parallel (parallel installation of machines in workstations if needed and installation of workstations in series). The objective was to determine the optimal values of the average processing time of each type of product in each workstation and the buffer volume of each station, with the goals of maximizing the production rate, minimizing the costs resulting from reducing the processing time, and the total volume of inter-station buffers simultaneously. To investigate the changes in the output rate of the production system, due to the high cost and time, it was deemed not cost-effective to implement the proposed changes on the system. Therefore, the combination of simulation techniques, design of experiments, and response surface methodology was used to fit the relevant metamodel. In the proposed approach of this research, taking a realistic view of production line modeling, the probability of machinery failure, as well as the possibility of repairability and return to the system, were considered in the form of statistical distribution functions. Additionally, all time parameters, including the arrival time between the parts, the start-up time of all the machines, the processing time, the time between two failures, and the repair time of the machines, were non-deterministic and subject to statistical distributions. Finally, to solve the structured mathematical model, two meta-heuristic algorithms (NSGA-II) and (NRGA) were considered.

This paper considers production scheduling and delivery problem together and is trying to integrate both of them. Retailers order includes different types of products that have been processed at a distribution center. After completing the order process at the distribution center, products are delivered to the retailer in window time; otherwise the distribution center is required to pay the penalty cost of window time to the retailer. The aim of solving this problem is to determine the production sequence, retailer need to heterogeneous vehicles and the visiting sequence of retailers with respect to the delivery window time. In this paper, a mixed integer linear programming model is provided in which the total cost, including the cost of vehicle routing, fixed costs and penalty costs have been minimized.in order to solve the proposed mathematical model , two approaches of CPLEX solver of GAMS software and Relaxation Lagrangian algorithm have been used. At first, by using different samples, the efficiency of Lagrangian Relaxation algorithm had been confirmed in small sizes, and then it is used in order to obtain an acceptable optimal solution in large sizes in a reasonable period.

In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing management and is regarded as one of the infrastructure and important concepts for implementing the career so that in many of them it is essentially tried to shorten the time between the customer’s order and the real time of delivering the goods. Cross docking is one of the most important alternatives for lowering the time in supply chain. The central aim of this paper is to focus on optimizing the planning of the trucks input and output aiming to minimize total time of operation inside the supply chain in designed model. Timing the transportation in this paper makes the time between sources and destinations, time of unloading and transferring the products minimized. To find the optimum answers to the question, genetic algorithms and the particle swarm optimization have been used. Then, these algorithms have been compared with the standards such as the implementation time and quality of answers with each other and then better algorithms in each standard identified.

the requirements for maintenance management have change drastically from the old concept of ‘fix-it-when-broken’ to a more complex approach, which entails adopting a maintenance strategy for a more integrated approach and alignment. This paper presents a Goal Programming (GP) approach to define the best strategies for the maintenance of some criticalpumps in a paper industry. For each pump failure mode, the model allows to take into account the maintenance policy burden in terms of inspection or repair and in terms of the manpower involved, linking them to efficiency-risk aspects quantified as in FMEA methodology through the use of the classic parameters occurrence (O), severity (S) and detectability (D), evaluated through an adequate application of the Analytic network process. (ANP) technique. An extended presentation of the data and results of the case analysed is proposed in order to show the characteristics and performance of this approach. The result is in the situation studied predictive and preventive maintenance obtain the best compromise between resource usage and reduction of failure effects. This fact is non-surprising since these policy allows failure prediction, thus providing the maintenance manager with useful information to limit the negative failure aspects.

This paper focuses on a novel model of the U-shaped assembly line balancing problem, in which the objective functions include cost, capacity, and quality. It is assumed that each task requires a set of equipment. In addition, the quality of tasks performed by each worker varies. Hence, the purpose of the model is that the total cost of the equipment is minimized and the quality of the work is maximized. Additionally, the number of workstations is minimized. At first, a multi-objective non-linear mixed-integer programming model is provided. Then, the model is linearized, and simulated annealing (SA) algorithm and two of its modified modes have been proposed to solve the problem. The proposed algorithm includes a new encoding/decoding scheme, as well as a local search for assigning the worker to each station. To determine the parameters in three algorithms, the experimental design has been used and various modes have been created by combining the parameters. Moreover, numerical examples were established based on the graphs found in the literature and the solution is compared with three algorithms, revealing the efficiency of each algorithm. Additionally, a case study on the nozzle assembly line in oil refineries was conducted to evaluate the efficiency of the proposed model and algorithm. Results from the case study show that the modified SA algorithms performed better.IntroductionNowadays, assembly lines play a crucial role in the production of standardized and high-volume products. If task allocation to workstations is done without considering the balance of the assembly line, it can lead to high levels of idle time in some workstations and decreased line efficiency. Therefore, assembly line balancing is an important stage in the production process to enhance production line productivity. This study focuses on the single-model U-shaped assembly line balancing problem. Assembly lines can be divided into four categories based on their layout, and in this research, the U-shaped assembly lines are specifically considered. The objectives of this problem include minimizing the number of workstations, minimizing equipment costs, and minimizing the level of work quality deviation at each workstation (equivalent to maximizing work quality). Additionally, constraints related to occurrence, precedence, and capacity, as well as limitations on tool and worker allocations, have been considered in the problem model. In terms of research gaps in this field, it should be noted that in previous studies on U-shaped assembly line balancing problems, objective functions combining cost, capacity, and quality have not been simultaneously addressed within a single problem. Furthermore, simultaneous allocation of workers (based on skill levels) and tools has not been studied in the context of U-shaped assembly line balancing problems.Materials and MethodsIn this study, a nonlinear mixed-integer multi-objective programming model is proposed for balancing a single-model U-shaped assembly line. The problem modeling assumes realistic conditions where each task requires a set of tools, and in this regard, the quality of task execution by workers is considered to be different. The modeling of quality in the assembly line balancing problem (as one of the objective functions) is approached differently compared to previous studies in this field, aiming to minimize the level of work quality deviation in all workstations. Additionally, for solving the problem, the allocation of workers and tools to the workstations is performed based on a neighborhood algorithm, which is a notable innovation in the research. In this study, a modified simulated annealing metaheuristic algorithm is developed with innovations in encoding and decoding procedures to solve the proposed model in three optimization scenarios. To compare the results of these algorithms, numerical examples based on graphs available in the research literature are solved using the three algorithms. Furthermore, a case study is conducted on the assembly line of component δ, which is used in oil refineries, to evaluate the efficiency of the proposed model and algorithm in real assembly lines.Discussion and ResultsIn this study, to validate the proposed algorithms, 10 numerical examples of different sizes (small, medium, and large) were designed based on valid graphs available in the research literature. Then, for various parameter values, each problem was solved 10 times using each algorithm, and the results of each algorithm were analyzed. In these examples, the costs of tools and the data related to task quality were randomly generated. Additionally, workers with different skills were defined to perform the tasks. Furthermore, the cycle time proportional to the activity durations was considered. It is observed that in all solved examples, the values of the third objective function (quality objective function) obtained from the third algorithm are better than the values obtained from the first and second algorithms. These results are not unexpected because in the third algorithm, due to the presence of an improvement loop for the third objective function, its value decreases compared to the other two algorithms, resulting in a reduction in the overall objective function and its improvement compared to the other two algorithms. For the cost minimization objective function (first objective function) and the number of workstations minimization objective function (second objective function), the values obtained from the three algorithms are approximately the same, and the difference in the obtained values for the overall objective function is primarily dependent on the value of the quality objective function (third objective function). Additionally, the results of solving the numerical examples show that the third algorithm achieves the best values for the overall objective function (compared to the other two algorithms) on examples with more than 25 activities, indicating that employing a local search for worker allocation in the modified simulated annealing algorithm makes the algorithm stronger and more efficient compared to its classical form.ConclusionIn this research, the modeling and problem-solving of the U-shaped assembly line balancing problem were investigated considering tool allocation constraints and quality conditions. To this end, a mixed integer nonlinear programming model was presented for the problem, where equipment and workers were simultaneously considered as two objectives in terms of minimizing equipment cost and the level of task quality. In addition to these two objectives, the number of workstations was also minimized. To solve the problem, a metaheuristic algorithm called simulated annealing was employed, as well as two improved versions of it (by introducing innovations in the random allocation of workers to workstations and applying a local search for improving worker allocation). The proposed model was solved using well-known graphs in the literature of assembly line balancing problems (as numerical examples) with the proposed algorithms, and the results obtained from the algorithms were compared and the performance of these algorithms was analyzed and examined.

Critical path method is one of the most widely used approaches in planning and project control. Time is considered a determinative criterion for the critical path. But it seems necessary to regard other criteria in addition to time. Besides time criterion, effective criteria such as quality, cost, risk and safety are considered in this paper. Then, the developed problem is solved as a multi-attribute decision making problem by a new extension of MULTIMOORA method. Moreover, type-2 fuzzy sets are utilized for considering uncertainties. Type-2 fuzzy sets are more flexible and capable than type-1 fuzzy sets in reflecting uncertainties. Eventually, SWARA method is developed for determining the weights of efficient criteria such as time, cost, quality, risk and safety under type-2 fuzzy environment. Finally, an applied example has been solved to illustrate the calculations and the ability of the proposed approach. Based on the example, it is clear that the longest path in terms of time criterion is not a critical path, and other influential criteria are involved in determining the critical path. IntroductionToday, in the competitive business environment, project management, planning, scheduling, and project control hold significant importance. One of the widely used and common methods in the field of project planning and control is undoubtedly the Critical Path Method (CPM). In the Critical Path Method, activity durations are predetermined. However, in the real world, many projects and activities are executed for the first time and have considerable uncertainties. Therefore, obtaining an accurate estimate of the time and resources required for activities is challenging. However, considering a single criterion, such as time, will not yield fruitful results, and other influential parameters such as risk should also be taken into account. For example, a path that carries a high level of risk may not be the critical path at present, but it may become critical in the future due to the high risk involved. For this reason, this research explores other influential criteria besides time and considers them in determining the critical path.Materials and MethodsIn this study, the problem under investigation is the determination of the critical path while considering other influential criteria in addition to the time criterion. To achieve this, multiple criteria decision-making methods are used to consider criteria such as time, cost, quality, risk, and safety in determining the critical path. Furthermore, to account for the uncertainties of the real world and incorporate expert opinions, type-2 fuzzy sets are utilized. It should be noted that the MULTIMOORA method is employed for ranking the critical paths, while the SWARA method is used to determine the weights of the influential criteria in determining the critical path. Both methods have been extended and developed in a type-2 fuzzy environment.Discussion and Results Initially, the proposed method is solved considering only the time criterion. As observed, the critical path has changed, indicating the importance of other criteria in determining the critical path. Then, the proposed method is solved considering pairwise combinations of the criteria, where the time criterion is treated as a fixed criterion due to its high importance. In fact, the problem is solved considering time and cost, time and risk, time and quality, and time and risk. By increasing or decreasing each criterion, the critical path changes, demonstrating the significance of all criteria in determining the project's critical path. To determine the critical path, it is necessary to consider all criteria together. These variations in the criteria and the resulting change in the critical path clearly indicate the importance and influence of other criteria in determining the critical path.ConclusionIn this article, an extension of the MULTIMOORA multi-criteria decision-making method is presented in the reference section. Additionally, Type-2 fuzzy numbers, which offer more flexibility and better representation of uncertainties compared to Type-1 fuzzy numbers, are utilized. The MULTIMOORA multi-criteria decision-making method is developed to incorporate these Type-2 fuzzy numbers. The opinions of three experts are used numerically for the time and cost criteria and linguistically as linguistic variables for the quality, risk, and safety criteria. Ultimately, the weights of the influential criteria of time, cost, risk, quality, and safety are determined using the developed SWARA method under Type-2 fuzzy environment. Finally, the most critical path is determined by considering not only the time criterion but also the influential criteria of cost, quality, risk, and safety. Based on the conducted research, a set of criteria including time, cost, quality, risk, and safety are used in this article, and additional criteria can also be added to this set.

Supplier selection is one of the most important problems in the field of management and optimization. It aims to optimize the cost of supply, quality of products and services, and the risk of non-supply, among others. However, existing models often overlook the risk of non-supply and the brand effect on demand. In this research, a supplier selection integer model is developed that considers lead time and the risk of non-supply. To solve this model, the LOKAD benchmark database is utilized, and a new adaptive variable neighborhood search algorithm is introduced, incorporating a scoring strategy to handle the model's complexity and obtain optimal or near-optimal solutions. The obtained Pareto solutions outperform traditional results, as confirmed by the Wilcoxon test. Sensitivity analysis of the solutions on the budget demonstrates that the final profit is more sensitive compared to lead time. Furthermore, distance from the ideal and diversity measures are used to quantitatively compare the results. IntroductionSupplier selection involves identifying, evaluating, and contracting with suppliers to meet an organization's requirements for raw materials and related infrastructure. It plays a crucial role in financial resource allocation and product/service quality. The main objectives of supplier selection include reducing purchasing risks, minimizing lead time, and enhancing quality. Many organizations face multiple criteria for selecting suppliers, such as receipt risk, green criteria, and lead time. Mathematical modeling and optimization techniques are commonly employed to achieve these objectives. However, existing models often lack real-case assumptions, motivating researchers to extend models in this environment. This research addresses these gaps by developing a mathematical model for supplier selection that considers the risk of non-supply and the substitution rate of inventory-less products. Materials and MethodsTechnically, the conducted modeling is a quantitative research in which data has been collected using library-based tools. Additionally, the statistical population corresponds to the LOKAD company, and a sample of its information is publicly available and will be utilized in the numerical computations phase of this research. This research develops a novel supplier selection multi-objective mathematical model that considers the risk of non-supply and substitution rate of demand when a product is inventory-less. Additionally, a modified adaptive variable neighborhood search (AVNS) algorithm is proposed to solve the model. While the focus of this paper is on the retail industry, the proposed model can be adapted to any industry. Discussion and ResultsThe developed model will be solved using data related to the LOKAD company, which was collected during a one-year period in 2018. This dataset includes sales and purchasing information of products related to the LOKAD company, encompassing details about the suppliers of the products and their brands. The utilization of this dataset is due to the fact that many articles have made use of it for its detailed information provided by the LOKAD company. In order to compare the method, the Wilcoxon test is used to compare two groups of variables, which can determine the presence of a difference between them. The observations reveal that the substitution rate of demand significantly affects the results and can alter the selection of final suppliers. Budget limitations are another important factor, where increasing the budget leads to higher profits by enabling the selection of more competent suppliers and high-quality products for customers. ConclusionsSupplier selection is a challenging problem in various industries. The experiments conducted in this research demonstrate that increasing the budget limitation results in higher profits, as customers prefer products or services with higher levels of quality. The developed mathematical multi-objective model incorporates real-case assumptions such as the risk of non-supply and substitution rate of demand. The model is solved using the proposed modified AVNS algorithm. The solutions obtained are analyzed using mean ideal distance and diversification metric measures to ensure their reliability. The analysis highlights the significance of budget limitations, which outweighs the impact of lead time in supplier selection problems. Ultimately, the model provides an optimal combination of suppliers. Additionally, the sensitivity analysis performed on the budget constraint reveals that changes in the budget have a greater impact on the final profit compared to lead time. The proposed model effectively determines the allocation of purchases from each supplier to enhance the final profit. In this regard, an initial estimation of future demand is considered as deterministic, although transforming this parameter into a probabilistic form can make the model more robust.

In this paper, the critical parameters of a method of welding with shielding gas arc welding (GMAW) are discussed; this method is an important process in creating high quality metal permanent connections in various industries, including the automobile industry to improve the quality of stem diameter welding parameters. One of the most useful techniques for modeling and solving the problems is Response Surface Method. In this paper, considering five most important factors such as speed welder, torch angle with the work piece, electrode diameter, wire speed, gas consumption ,and CO2 levels as input variables, can be controlled independently from the level of response, the relationship between the input variables and the response variables were determined using linear regression. Then optimum value for each factor was calculated using non-linear programming model to evaluate the results obtained along with the comparison of output of the Simulation Annealing Algorithm. In this study, both qualitative and quantitative variables are considered to evaluate and optimize all response variables regarding that these variables are not the same, and then fuzzy set theory and LP metric are used to find answers for multi-objective optimization methods.

This paper presents a multi-objective mathematical model for redundancy allocation in production systems. In many of the production and assembly lines, process times, time between failures and repaired times are generally distributed. The proposed method of this paper is able to consider time dependent parameters as general distribution functions by using the hybrid approach of simulation and response surface methodology. The objectives of the mathematical model are maximizing production rate, minimizing total cost and maximizing quality. In order to solve the proposed mathematical model, non-dominated sorting genetic algorithm and multiple objective particle swarm optimization are used. Numerical results indicate the effectiveness of both algorithms for generating non-dominated solutions. Moreover, comparative results indicate the superiority of the Non-dominated sorting genetic algorithm.

In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, the second one is to determine a worker among a set of skilled workers for processing each operation on the selected machine and the third one deals with sequencing the assigned operations on the machines considering workers in order to optimize the performance measure. In this paper, we provide a mathematical model for this problem and then propose a hybrid meta-heuristic algorithm for solving the problem. The proposed hybrid algorithm uses variable neighborhood search and simulated annealing algorithms to search in the solution space. Computational study with randomly generated test problems is performed to evaluate the performance of the proposed algorithm. The results show the proposed algorithms are effective approaches for solving the DRCFJSP.

The Customs Tariff is based on the World Customs Organization's (WCO) Harmonized Commodity Description and Coding System (HS) and its impact of industry competitiveness is known for everyone. In this paper, by considering IRAN I.R. industries,the impact of change in custom tariff on domestic industries have been analyzed. Many models have been used to analys and evaluate the effect of change in custom tariff on domestic industries. In this paper System Dynamics(SD) approach and modeling has been used to describe this problem. By using Casual Loops(CL) and also Stock And Flow diagram(CFD), the model of problem has achived. At next step, depending on the type of variables(State variable, Rate variable, Auxiliary variable, parameters, …) the relation of variables are identified and the model is completed. At final step the model was sumilated and run by VENSIM software and the results were outcomed. This paper declares that decreasing the custom tariff will improve domestic industries competitiveness in long terms by developing the infrastructure of domestic industry for gaining more market share

For performance evaluation of supply chain, having a comprehensivemodel with reliable data is useful. This can help to improve the entirechain. In this paper a model is presented, according to the nature ofnetwork and multi-stage supply chain, that able to evaluate theperformance of the entire chain in the form of a mathematical modeland using the financial, knowledge, participation and responsemeasures of the supply chain. In the first part, Indicator sat threelevels; strategic, process and operational, considered and survey themodel verification with Factor Analysis. In the second part, Networkdata envelopment analysis model is used. This paper is the result ofresearch related to supply chain of pharmaceutical companies inTehran Stock Exchange and 115 expert sand senior executive shavebeen questioned as sample. There search results show that strategiclevel with a weight of 0.98 is the most important performance leveland Process and operational levels are respectively 0.97 and 0.87weight. 4chainsof 28 chains studied, have a complete performance and0.43 is the lowest observed performance.

Project cost estimation and actual time is one of the serious concernsof project managers. Earned Value Management is considered as atechnique for answering two following questions: how long does ittake to complete a project? How much does the project cost? ProjectPerformance Evaluation can only be predicated based on previousdata and knowledge in the present dealings of earned value and itsfuture conditions and prospects would not be categorically realized.Although there are many proposed methods to solve these boundaries,there is not a method encompassing all uncertainties and futureconditions. This paper aims at introducing a new method using timelimits, costs and project performance in fuzzy logic to determine theearned value indexes. Risk factors are added to the Fuzzy logics inorder to obtaining the precise estimation in predicting the finaloutcomes of the project. This paper provides a brief introductionregarding the current methods followed by investigation on Fuzzymethodology and risk factors. Finally, the offered model is proposed,defined and supported by means of an example

In this paper, a Decision Support System (DSS) is developed in order to evaluate customers. The addressed system can specially be applied in captive markets. This paper extends Chamodrakas et al. [8] in which the customer evaluation in order to customer selection is executed using a fuzzy TOPSIS method. The proposed DSS includes 6 different models and the user can select among them. The system is coded utilizing C# programming language. To present the system performance, some parts of the system are presented along with a numerical problem.

Today, foreign investment is known as one of the most important sources of funding and technology transfer modes in developing countries, that is because foreign investment, in comparison to domestic investment, includes competitive advantages such as accessibility to sufficient information, managerial skills, advanced technology and valid sources of funding., Due to high demand of large expenses and advanced technologies, foreign investment is a necessary and inevitable issue in oil refinery industries. In related to multiplicity of foreign investment modes, selecting the right mode of investment has been as one of the strategic decisions for investee countries. The present research, therefore, is designed to identify and prioritize the different modes of foreign investment in oil refinery industries. This paper is based on a research, descriptive-survey methodology. To analyze the gathering data used in this paper is based on statically and multinational attribute decision making techniques. The final results illustrates that using investment mode like Built, Operation, Transfer (BOT) has been considered to be the best method in comparison with other modes in this oil refinery industry.