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Abstract This article examines the influence of various factors on the precision of 3D printed objects produced through Fused Filament Fabrication (FFF) using Polylactic Acid (PLA) as the primary material. While PLA is widely chosen for its compatibility with biological applications, it does suffer from limited dimensional accuracy. The study focuses on investigating the effects of four independent parameters, namely layer height, infill density, printing speed, and the number of top and bottom layers, on accuracy along the three axes of length, width, and depth. The research objective is to identify the optimal levels of these parameters that minimize dimensional errors across all axes. However, due to conflicting requirements among different dimensions, determining a comprehensive decision-making methodology presents a significant challenge. To address this issue, three distinct approaches are proposed, each offering a unique perspective and practical application. Interestingly, the outcomes of these approaches converge on a single solution. Among these parameters, printing speed emerges as the dominant factor, while, the number of layers is the least influential. Moreover, consistent shrinkage is observed in the length and width dimensions, with greater errors evident in longer dimensions. Overall, a preference for lower layer height and infill density, along with higher printing speed and increased top and bottom layers, is recommended. The study findings indicate that the most effective parameter selection for reducing errors in all dimensions involves a layer height of 0.2 mm, infill density of 60%, printing speed of 50 mm/s, and the use of four top and bottom layers.

Introduction: Despite significant technological advancements, today's world still grapples with various natural and man-made disasters, such as earthquakes, floods, hurricanes, avalanches, wars, terrorism, and political unrest. These events not only impede sustainable development but can also cause severe and sometimes irreparable damage to the well-being and prosperity of communities. This necessitates an integrated logistics system, scientifically and comprehensively designed to meet crisis management needs. Such a system must have clear, predefined processes where all components function according to predetermined roles. Providing aid to disaster victims is a crucial stage of crisis management that must be planned before the occurrence of an event. Timely and efficient aid significantly reduces human and financial losses. Therefore, appropriate pre-crisis planning is essential to avoid being caught unprepared during natural disasters. Humanitarian logistics (HL) is one of the most critical issues in disaster operations and management. HL operations must be sustainable enough to function effectively under the uncertain and complex nature of disasters and crises. Many challenges in pre- and post-disaster phases lead to human and economic losses, making efficient design of HL operations essential. This study reviews articles published between 2004 and 2023 to examine optimization models for locating humanitarian logistics facilities and centers. The purpose is to understand current research trends in HL, particularly the optimization methods used for facility location, and to provide directions for future research.Methods: To gain an overview of the research landscape and identify relevant articles and key researchers, the Web of Science database was used to search for pertinent keywords. This study includes all types of facility location problems and classifies the reviewed articles into deterministic and non-deterministic models. In the deterministic models table, the type of objective function, decision variables, model type, and solution methods are detailed. For non-deterministic models, the study focuses on stochastic programming and robust optimization approaches. The non-deterministic models table includes the type of objective function, decision variables, non-deterministic parameters, type of uncertainty, model type, and solution methods.Results and discussion: The review identified 19 factors contributing to the decline of companies, from the 22 factors previously identified in the literature. Factors such as "market monopoly status," "oversupply," and "lack of cooperation culture among employees and managers" were excluded due to their lower importance, as determined by experts. For these identified factors, 32 localized and new strategies were determined and categorized into six groups: financial and economic strategies; marketing and customer orientation; human resources; knowledge-based strategies; structure and interactions; and production and operations efficiency. The hierarchical interpretive structural model of revitalization strategies indicates that these strategies are interdependent, helping and facilitating each other. Effective implementation should start from the lowest level of the model. Notably, although the strategies are categorized, the model shows that it is unnecessary to focus on all strategies within a single category simultaneously. Instead, the hierarchy clearly demonstrates the priority order from the bottom up and across different categories.Conclusions: Reviving declining and bankrupt small and medium-sized companies, particularly in the food industry, is not a one-dimensional process and does not have a single strategy. Instead, it requires a combination of six strategy categories: financial and economic; marketing and customer orientation; human resources; knowledge-based strategies; structure and interactions; and production and operations efficiency. The revival process must be systematic, continuous, and gradual. This study can help researchers understand current optimization trends in HL and identify research gaps to contribute to societal well-being through their research.

In this study, we attempted to conduct a comprehensive review of the existing and pertinent literature on the topic of factor investment. We performed Scientometric analysis of studies published in reputable finance journals, i.e., The Journal of Portfolio Management, The Financial Analysts Journal, The Journal of Asset Management and others, during the years 2014 to 2023. To obtain the research data for our study, we gathered and examined a collection of 76 bibliographic records sourced from the Web of Science database. This database provided a comprehensive and reliable source of scholarly publications in the field of finance. To analyze the data, we employed Scientometric networks as part of our analytical approach. Scientometric networks allowed us to explore the relationships and connections between different publications, authors, and keywords within the domain of factor investment. To visualize and present the research findings, we utilized the Bibliometrix package for R, a powerful tool specifically designed for bibliometric analysis. This package enabled us to generate insightful visualizations that showcased the key patterns, trends, and interconnections within the literature on factor investment. By employing Scientometric analysis and leveraging the capabilities of the Bibliometrix package, we aimed to provide a comprehensive overview of the existing scholarly research in this field and contribute to the understanding of factor investment.

This paper addresses the imperative task of assessing and ranking cryptocurrencies, particularly pertinent in the context of the burgeoning popularity of public blockchains. The proliferation of available options necessitates a rigorous evaluation, prompting the formulation of a novel model grounded in both objective and subjective criteria. To contend with the challenge posed by the expanding landscape of public blockchains, ten discerning criteria are delineated, encompassing facets such as Technology, TPS, Market capitalization, GitHub fork, GitHub stars, Twitter followers, Twitter hashtags, trading volume, sentiment score, and the price range differential. Leveraging expert opinions, the pairwise impact of these criteria is ascertained, and the DEMATEL method is judiciously employed to derive their respective weights. Subsequently, the PROMETHEE method is harnessed to effectuate the ranking of 20 cryptocurrencies predicated on the identified criteria. Furthermore, the integration of LSTM enables the prediction of values for four predictable criteria, seamlessly incorporated into the PROMETHEE model to furnish rankings across diverse temporal intervals. The proposed model, thus, presents a holistic and pragmatic approach to inform investment decision-making within the dynamic cryptocurrency market. By embracing a comprehensive set of criteria and integrating predictive analytics, this model stands as a valuable contribution to the field, offering nuanced insights to stakeholders navigating the complexities of cryptocurrency investment.

Since 1952, when the mean-variance model of Markowitz introduced as a basic framework for modern portfolio theory, some researchers have been trying to add new dimensions to this model. However, most of them have neglected the nature of decision making in such situations and have focused only on adding non-fundamental and thematic dimensions such as considering social responsibilities and green industries. Due to the nature of stock market, the decisions made in this sector are influenced by two different parameters: (1) analyzing past trends and (2) predicting future developments. The former is derived objectively based on historical data that is available to everyone while the latter is achieved subjectively based on inside-information that is only available to the investor. Naturally, due to differences in the origin of their creation the bridge between these two types of analysis in order to optimize the portfolio will be a phenomenon called "ambiguity". Hence, in this paper, we revisited Markowitz's model and proposed a modification that allow incorporating not only return and risk but also incorporate ambiguity into the investment decision making process. Finally, in order to demonstrate how the proposed model can be applied in practice, it is implemented in Tehran Stock Exchange (TSE) and the experimental results are examined. From the experimental results, we can extract that the proposed model is more comprehensive than Markowitz's model and has greater ability to cover the conditions of the stock market.

This paper has investigated the inventory and financial flows in supply chains. Its purpose is to provide a method to optimize these two flows for chain members, where Return on Capital (ROC) is defined as the dependent variable, and cash conversion cycle (CCC) equation components, which show financial and physical flows, are formulated as independent variables. The data of chain members from six selected industries, including auto & parts, pharmacy, food, petrochemical, metal, and mining, have been extracted. Two scenarios, 1) revision of independent variables without a change in the cash conversion cycle of the entire supply chain, and 2) reducing the days of independent variables along with reducing the cash conversion cycle, have been defined. The problem is simulated using Agent-Based Modeling and NetLego software. Results of the first scenario indicate that if Days Inventory Outstanding (DIO) is reduced in downstream and transferred to upstream of the chain, and Days Payment Outstanding (DPO) in the upstream is shortened, ROC is improved for the entire chain. Also, the results of the second scenario show that, in proportion to the reduction of the cash conversion cycle through productivity under collaboration of chain members, the performance improvement of ROC is remarkable.

Objective Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. Methods Considering the dynamic nature of the problem, the dynamic multi-criteria decision-making model was implemented based on the real data of international freight transportation (including export, import, and transit) by international rail transport companies in five years. To estimate future data for international rail transport companies, the fuzzy inference system was used. A fuzzy inference system is a mathematical tool that can handle uncertainty and imprecision in data. Results Considering the dual uncertainty factors initially, companies' activities were evaluated using a fuzzy inference system, followed by the dynamics of a dynamic multi-criteria decision-making model the prioritization of 15 international rail transport companies for the Islamic Republic of Iran Railways was established. The results indicated that dealing with the problem of ranking international rail transport companies dynamically will achieve a more favorable outcome. The applicability of the presented method to benefit from the fuzzy inference system in the dynamic multi-criteria decision-making model is one of the other results. Conclusion This study could develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. The proposed dynamic multi-criteria decision-making model includes several implications. Firstly, it provides a comprehensive and systematic approach to evaluate and rank international rail transport companies based on multiple criteria and factors. This approach can help decision-makers to make informed decisions and allocate resources effectively. Secondly, the use of the fuzzy inference system to estimate future data is a significant contribution to the field of transportation. This mathematical tool can handle uncertainty and imprecision in data, which is common in transportation systems. This can help decision-makers to anticipate future trends and plan accordingly. Moreover, the proposed model can be customized and applied to other realms and fields. The model can be adapted to the specific needs and requirements of each problem, making it a versatile and adaptable tool for decision-making. By using this model, decision-makers can take into account multiple criteria and factors, such as cost, time, and environmental impact, and evaluate different scenarios to find the most optimal solution. Objective Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. Methods Considering the dynamic nature of the problem, the dynamic multi-criteria decision-making model was implemented based on the real data of international freight transportation (including export, import, and transit) by international rail transport companies in five years. To estimate future data for international rail transport companies, the fuzzy inference system was used. A fuzzy inference system is a mathematical tool that can handle uncertainty and imprecision in data. Results Considering the dual uncertainty factors initially, companies' activities were evaluated using a fuzzy inference system, followed by the dynamics of a dynamic multi-criteria decision-making model the prioritization of 15 international rail transport companies for the Islamic Republic of Iran Railways was established. The results indicated that dealing with the problem of ranking international rail transport companies dynamically will achieve a more favorable outcome. The applicability of the presented method to benefit from the fuzzy inference system in the dynamic multi-criteria decision-making model is one of the other results. Conclusion This study could develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. The proposed dynamic multi-criteria decision-making model includes several implications. Firstly, it provides a comprehensive and systematic approach to evaluate and rank international rail transport companies based on multiple criteria and factors. This approach can help decision-makers to make informed decisions and allocate resources effectively. Secondly, the use of the fuzzy inference system to estimate future data is a significant contribution to the field of transportation. This mathematical tool can handle uncertainty and imprecision in data, which is common in transportation systems. This can help decision-makers to anticipate future trends and plan accordingly. Moreover, the proposed model can be customized and applied to other realms and fields. The model can be adapted to the specific needs and requirements of each problem, making it a versatile and adaptable tool for decision-making. By using this model, decision-makers can take into account multiple criteria and factors, such as cost, time, and environmental impact, and evaluate different scenarios to find the most optimal solution.

Abstract In manufacturing systems, simulation modeling plays an important role in creating some changes instead of working on real systems. Manipulation in a real system is more costly than manipulation in a simulated model. In this research, we tried to use a simulation approach to recognize and minimize bottlenecks of a production line, which will decrease costs and improve productivity. To achieve our objectives, we chose a case and analyzed its production line. By using our case study strategy, we tried to collect our data and adapt a conceptual model of production processes drawing on an operation process chart (OPC). After that, we created a simulation model of the production processes by using the popular Arena simulation software 13.5. By running the Arena model, some bottlenecks were found. Ultimately, we proposed some solutions to obviate bottlenecks and reduce the total costs of production.

Purpose: Non-cancerous prostate lesions such as prostate calcification, prostate enlargement, and prostate inflammation cause too many problems for men’s health. This research proposes a novel approach, a combination of image processing techniques and deep learning methods for classification and segmentation of the prostate in CT-scan images by considering the experienced physicians’ reports. Methodology: Due to the various symptoms and nature of these lesions, a three-phases innovative approach has been implemented. In the first phase, using Mask R-CNN, in the second phase, considering the age of each patient and comparison with the standard size of the prostate gland, and finally, using the morphology features, the presence of three common non-cancerous lesions in the prostate gland has investigated. Findings: A hierarchical multitask approach is introduced and the final amount of classification, localization, and segmentation loss is 1%, 1%, and 7%, respectively. Eventually, the overall loss ratio of the model is about 9%. Originality: In this study, a medical assistant approach is introduced to increase diagnosis process accuracy and reduce error using a real dataset of abdominal and pelvics’ CT scans and the physicians’ reports for each image. A multi-tasks convolutional neural network; also presented to perform localization, classification, and segmentation of the prostate gland in CT scans at the same time.

In Home Health Care (HHC) operations, one of the major aims of HHC centers is to timely meet patients' demands. According to the feedback from HHC centers, their decision-makers deal with some complexity in scheduling and routing of their health workers. Inspired by this point, the present research introduces a new HHC routing and scheduling problem considering different skill levels of health workers and different levels of patients’ needs. So, in such a condition, a highly qualified health worker can visit those patients who need lower-skilled demands while a low-qualified health worker cannot visit those who request higher skills. In this way, the total cost of the system will be lower compared to the situation in which the patients' needs exactly match the health workers' skills. Moreover, we consider that the maximum number of homes each health worker is tasked to visit during the day is specified and if more patients than this specified limit are assigned to each health worker, an additional cost will be imposed on the center in proportion to the excess number of patients. Since patient satisfaction, which is obtained with timely visits, is important for each HHC center, a hard time window is considered for each patient. The presented model is solved using the GAMS software with the CPLEX solver. Along with the MIP approach, a metaheuristic algorithm based on a Simulated Annealing (SA) algorithm is adopted to solve the problem. The results give the managers insight into this method of cost management in comparison with manual and traditional traditional planning. This study may help the decision-makers of HHC centers make more accurate decisions which, in turn, result in timelier service provision, increase the patients' satisfaction level, and improve the overall efficiency of HHC centers.

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors; the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model’s performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample problems with different dimensions, as well as a case study on residential apartment construction projects by a contracting company. In this respect, the answers obtained from solving the model using the multi-objective DE algorithm were better and superior to the NSGA-II algorithm and had a more favorable performance. Generally, the results indicate that using the integrated multi-objective mathematical model in the present research for managing and scheduling multi-mode resource-constrained multi-project problems, especially in the construction industry, can lead to an optimal state consistent with the desired objectives and can significantly improve the progress and completion of projects.

Data envelopment analysis (DEA) measures the relative efficiency of decision-making units (DMU) with multiple inputs and multiple outputs. In the case of considering a working team as a DMU, it often comprises multiple positions with several employees. However, there is no method to measure the efficiency of employees individually taking account the effect of teammates. This paper presents a model to measure the efficiency of employees in a way that they are fairly evaluated regarding their teammates’ relative performances. Moreover, the learning expectations and the effect of learning lost due to operation breaks are incorporated into the DEA model. This model is thus able to rank the employees working in each position that can then be utilized within award systems. The capabilities of the proposed model are then explored by a case study of 20 wells with 160 distinct operations in the South Pars gas field, which is the first application of DEA in the oil and gas wells drilling performance analysis.

Today, the required energy mostly comes from fossil fuels. Due to the limitation of fossil fuel reserves in the world and emissions of pollutants, today's industries have been challenged to replace renewable energy source.Among these renewable energies, solar and wind are important. In this research, firstly, the factors affecting the location of the solar and distributed generation have been investigated and mapping of criteria in the GIS has been prepared.Then, considering the importance of integrating the information, the ANP technique is chosen for weighting the layers and implemented with Super Decision software.Finally, the model of supply chain of the distribution network is proposed with the aim of maximizing the supplier's profit and minimizing the emissions.Zanjan province is considered as the case study for which the model is solved. According to the results, areas of Khodabandeh, Ijrud, and Mahnashan are suitable for the construction of wind power plants and areas of Khodabande and Ijrud are suitable for the construction of solar power plant. Finally, Khodabandeh, Zanjan, and Mahnashan are suitable for construction of the small gas scale plant.

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.

Portfolio optimization is one of the most important issues in financial markets. In this regard, the more realistic are assumptions and conditions of modelling to portfolio optimization into financial markets, the more reliable results will be obtained. This paper studies the knapsack-based portfolio optimization problem that involves discrete variables. This model has two very important features; achieving the optimal number of shares as an integer and with masterly efficiency in portfolio optimization for high priced stocks. These features have added some real aspects of financial markets to the model and distinguish them from other previous models. Our contribution is that we present an algorithm based on dynamic programming to solve the portfolio selection model based on the knapsack problem, which is in contrast to the existing literature. Then, to show the applicability and validity of the proposed dynamic programming algorithm, two case studies of the US stock exchange are analyzed.

Due to the development of biomedical equipment and healthcare level, especially in the Intensive Care Unit (ICU), a considerable amount of data has been collected for analysis. Mortality prediction in the ICUs is considered as one of the most important topics in the healthcare data analysis section. A precise prediction of the mortality risk for patients in ICU could provide us with valuable information about patients' lives and reduce costs at the earliest possible stage. This paper aims to introduce a new hybrid predictive model using the Genetic Algorithm as a feature selection method and a new ensemble classifier based on the combination of Stacking and Boosting ensemble methods to create an early mortality prediction model on a highly imbalanced dataset. The SVM-SMOTE method is used to solve the imbalanced data problem. This paper compares the new model with various machine learning models to validate the efficiency of the introduced model. The achieved results using the shuffle 5-fold cross-validation and random hold-out methods indicate that the new hybrid model has the best performance among other classifiers. Additionally, the Friedman test is applied as a statistical significance test to examine the differences between classifiers. The results of the statistical analysis prove that the proposed model is more effective than other classifiers. Furthermore, the proposed model is compared to APACHE and SAPS scoring systems and is benchmarked against state-of-the-art predictive models applied to the MIMIC dataset for experimental validation and achieved promising results as it outperformed the state-of-the-art models.

The most significant international issue affecting society and the economy today is energy. Also, addressing sustainability issues, including environmental, economic, and social issues are the main goals of governments in developing and developed countries. Therefore, it has been in the interest of governments to consider strategies that could move the energy industry, especially fuel, towards clean fuel production. In this paper, the effect of government financial interventions under the coalition of bio-refineries to achieve sustainability goals has been studied. The proposed nonlinear model aims to obtain the optimum amount of biofuel production under the conditions of the cooperation of bio-refineries and government financial interventions. On the other hand, government sustainability goals are considered as functions related to economic, social, and environmental issues. Mulvey's robust approach is employed for controlling model uncertainties. For finding the best tariff policy, the MULTIMOORA approach is used by considering sustainability considerations, and the Best-Worst Approach is followed in obtaining the weights of the criteria. Furthermore, for the best tariff policy in a general coalition, sharing the profits among the bio-refineries as players in a cooperative game, fair profit distribution methods were applied. Finally, managerial results were presented by a sensitivity analysis of the tariff parameter.

With regard to the industrychr('39')s development, occupational safety is a key factor in protecting the workerchr('39')s health, achieving organizational goals and increasing productivity. Therefore, research is needed to investigate the factors affecting occupational safety. This research, based on the information gathered from the paint halts of one of the industrial units of Tehran, uses data mining technique to identify the important factors.Initially with Literature review to 2018, an insight into existing approaches and new ideas earned. Then, with a significant 5600 units of data, the results of the charts, association rules and K-means algorithm were used to extract the latent knowledge with the least error without human intervention from the six-step methodology of Crisp for data mining.The results of charts, association rules, and K-means algorithm for clustering are in a line and have been successful in determining effective factors such as important age groups and education, identifying important events, identifying the halls and finally, the root causes of major events that were the research questions.The results reveal the importance of very young and young age with often diploma education and low experience, in major accidents involving bruising, injury, and torsion, often due to self-unsafe act and unsafe conditions as slipping or collision with things. In addition, the important body members, hands and feet in the color retouching and surface color cabins are more at risk. These results help improve safety strategies. Finally, suggestions for future research were presented.

Financial decision-making is the principal part of any decisions hence great efforts are done to improve the methods to assess and analyze the stock in financial markets as a part of the financial decision. This paper addresses the stock selection by discovering investorchr('39')s utility function .Investors in the Stock Exchange consider diverse criteria to buy shares and bonds. Due to the criteria development in stock selection, understanding the investorchr('39')s behavior by a consultant is a prominent issue. Recognizing an exclusive utility function according to the characteristics of the investors facilitates acquiring each sharechr('39')s value for the decision maker (DM) when it is required. In this study, UTASTAR method is used to estimate the marginal value function, using 3 appropriate criteria (risk, return, liquidity) and finally fit out the total utility function. It provides the opportunity to make a rational decision fit to investorchr('39')s mentality and allowing their ranking, prioritization, selection or classification. The ranking of the options is as compatible as possible to the original one. The method is applied to an example from Iran Stock Exchange.

Tehran, as one of the most populated capital cities worldwide, is categorized in the group of highly polluted cities in terms of the geographical location as well as increased number of industries, vehicles, domestic fuel consumption, intra-city trips, increased manufacturing units, and in general excessive increase in the consumption of fossil energies. City logistics models can be effectively helpful for solving the complicated problems of this city. In the present study, a queuing theory-based bi-objective mathematical model is presented, which aims to optimize the environmental and economic costs in city logistics operations. It also tries to reduce the response time in the network. The first objective is associated with all beneficiaries and the second one is applicable for perishable and necessary goods. The proposed model makes decisions on urban distribution centers location problem. Subsequently, as a case study, the fruit and vegetable distribution network of Tehran city is investigated and redesigned via the proposed modelling. The results of the implementation of the model through traditional and augmented ε-constraint methods indicate the efficiency of the proposed model in redesigning the given network.

In today complex worldwide supply chains, intermediary organizations like Contract manufacturers and GPOs are mostly used. Well-known OEMs delegate their purchasing and procuring to these intermediaries. Because of their positive influence on supply chain efficiency, it is very important to investigate the role of intermediaries in today competitive supply chains. One important question arising about intermediaries is the conditions that the OEM controls his procurement or delegates this task to the intermediary organization? To answer this question, this paper studies the equilibrium for component procurement strategies of two competing OEMs that produce substitutable products. Each OEM may either directly procure the input from the component supplier, or delegate the procurement task to the contract manufacturer. We analyze the OEMs’ procurement game under two contracting power schemes in such a supply chain: the supplier Stackelberg, where the component supplier acts as the Stackelberg leader, and the OEM Stackelberg, where the OEMs are the first movers. We show that, the smaller OEM always prefers direct control of component procurement. This is because the OEM will receive a lower component price if the component supplier can price discriminate the OEMs. In contrast, the larger OEM’s preference depends on the contracting power scheme. Under the supplier Stackelberg, the larger OEM never prefers direct procurement; however, under the OEM Stackelberg, the larger OEM may have incentives to use direct procurement under reasonable conditions. This implies that a shift of the market power from the supplier to the OEMs may lead to more OEMs deviating from delegation to direct control.

Abstract: A project portfolio is a crucial decision-making process used to prepare an optimum collection of vast alternative projects. In most of the previous modeling methods, the focus is directed towards maximizing project efficiency and so, the role of risky aspects in selecting appropriate projects has been neglected. This paper presents an integrated multi-objective mathematical programming (MOMP) based on efficiency-risk for selecting a project portfolio using various techniques including data envelopment analysis (DEA), risk priority number (RPN) and non-dominated sorting genetic algorithm (NSGA-ΙΙ). The proposed model can support both the capability to nominate mutually exclusive projects (conflicting projects that only one of them can be done) or any type of predecessor projects (doing a project depends on another project) and concurrent projects (need to be done at the same time) selection. Another advantage of the model is that the hyper heuristic solutions can be found in the form of several non-dominated cases and it is possible for organization’s experts to choose the best and the most suitable solutions. Introduction: In the competitive world, intelligent optimum decision making is a vital task in the success of large systems. Due to lack of resources, it is not always possible to fully evaluate all proposed developmental projects. In this context, it is important to make an optimal portfolio of desired projects. The present article aimed to identify an applied methodology for the selection of project portfolios with regard to efficiency and risk assessment and the alignment of the projects’ purposes with the organization’s goals, while also considering the existing resource limitations and regarding risk as a main indicator. The efficiency of the projects is examined with regard to the resources used. The present paper seeks to respond to the following main questions: ü How can an optimal project portfolio be selected for an organization with maximum efficiency and minimum risk? ü Does project risk assessment in view of the two criteria of impact and probability sufficiently address the economic conditions and the complexity of the projects in the majority of research and development projects? ü What is the drawback of current risk assessment methods for project portfolios? Responding to these questions can help organization managers select optimal project portfolios and achieve their strategic organizational objectives. Many studies have been conducted on the selection of project portfolios and most of them follow a qualitative and quantitative approach and use combination classification. One of the applied studies conducted on project selection is by Eilat et al., (2008), in which projects were selected within the combinational framework of DEA and a balance score card was used to select proper research and development projects (Tahri, 2015) presented two numerical methods for mathematical optimization problems for both single and multiple objectives using two values (0 and 1) as the decision variable. (Huang et al., 2016) discussed the joint problem of optimal project selection and scheduling in situations when the projects’ initial outlays and net cash inflows are determined by experts’ estimates due to the lack of historical data. A literature review reveals that there are a lot of optimization models available to prepare project portfolios. Most of them have one objective function to maximize project benefits or to minimize operating costs subject to operational constraints. In most modeling methods, the role of risk aspects in selecting appropriate projects has been neglected. A few of them have explored the aspect of model constraints. In other words, in the past, the main focus is directed towards economic projects while the sustainable factors (e.g., environmental and social risks) have been neglected. For this purpose, this study presents an integrated MOMP based on efficiency-risk for selecting a project portfolio using various techniques including DEA, RPN and non-dominated sorting genetic algorithm (NSGA-ΙΙ). Materials and Methods: Step1) Preparing a list of candidate & feasible projects Step2) Calculating efficiency of each project by using the DEA method Step3) Calculating the risk priority number for each project Step4) Developing a MOMP model to select the best projects Step5) Solving the model using a Non-dominated Sorting Genetic Algorithm ΙΙ (NSGAΙΙ) method Results and Discussion: One of the main advantages of NSGAII is that the at-hand solutions can be found in the form of several non-dominated cases and therefore, it is possible for organization’s experts to choose the best and the most suitable solutions. Stated differently, the expert’s knowledge and viewpoints can be considered due to flexibility and availability of different solutions. Besides, it is feasible to compute the adjusted efficiency and to select the solution(s) producing the maximum efficiency as the final optimal answer(s). Conclusion: This article used the concepts of DEA, RPN, MOMP and NSGAII modeling to propose an applied methodology for extracting an optimal portfolio of projects. In other words, we suggest the use of a Non-dominated Sorting Genetic Algorithm ΙΙ as a solution method for the presented multi-objective model in order to deduce the optimal projects portfolio. By comparing the results for the proposed algorithm and existing methods, it was concluded that the previous methods can give portfolio of projects with suitable profit but these solutions face high risk and low reliability. Thus, such solutions are not acceptable by managers in organizations. The lack of attention to risk aspects leads to the non-realization of the estimated profit due to high probability of risk. Therefore, the valuable resources will be wasted. Whereas, by adjusting profit with the risk numbers, it is indicated that the profit gained by our method is so better and higher than previous methods after risk. This can indicate novelty, applicability, and performance of our method. References Eilat, H., Golany, B. & Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36, 895-912. Huang, X., Zhao, T. & Kudratova, S. (2016). Uncertain mean-variance and mean-semivariance models for optimal project selection and scheduling. Knowledge-Based Systems, 93, 1-11. Tahri, H. (2015). Mathematical Optimization Methods: Application in Project Portfolio Management. Procedia-Social and Behavioral Sciences, 210, 339-347.

This paper presents a competitive supply chain network design problem in which one, two, or three supply chains are planning to enter the price-dependent markets simultaneously in uncertain environments and decide to set the prices and shape their networks. The chains produce competitive products either identical or highly substitutable. Fuzzy multi-level mixed integer programming is used to model the competition modes, and then the models are converted into an integrated bi-level one to be solved, in which the inner part sets the prices in dynamic competition and the outer part shapes the network cooperatively.Finally, a real-world problem is investigatedto illustrate how the bi-level model works and discuss how price, market share, total income, and supply chain network behave with respect to key marketing activities such as advertising, promotions, and brand loyalty.

Nowadays, projects have become so widespread in the world that individuals and organizations are always involved in a variety of them. Recent advances in technology and fundamental chang-es in most scientific disciplines have had an essential impact on projects, and have made the na-ture and environmental conditions governing them to become more complex than before. With increasing complexity, the amount of information needed for project management increases. In general, the increasing complexity of projects is a growing source of project risks. It has been recognized that complexity affects the performance of a project and will be effective in its suc-cess. In this context, the traditional principles and practices of project management are no longer able to control the emerging complexity of projects. In addition, one of the key factors for the success of the projects is the appropriateness of the project manager's assignment. Many studies have been carried out in identifying the suitability criteria of the project manager and the methods of selecting the project manager. In most of these studies, the amount and type of complexity of the project are mentioned as factors influencing the design of an appropriate project manager. However, there has not yet been a specific approach for selecting the project manager with re-gard to the complexity of the project. Therefore, in this research, we try to investigate the rela-tionship between the complexity of the project and the merits of the project manager by applying a structural equation modeling approach.

Abstract This paper presents the competitive supply chain network design problem in which $$n$$ n decentralized supply chains simultaneously enter the market with no existing rival chain, shape their networks and set wholesale and retail prices in competitive mode. The customer demand is elastic and price dependent, customer utility function is based on the Hoteling model and the chains produce identical or highly substitutable products. We construct a solution algorithm based on bi-level programming and possibility theory. In the proposed bi-level model, the inner part sets the prices based on simultaneous extra- and Stackleberg intra- chains competitions, and the outer part shapes the networks in cooperative competitions. Finally, we use a real-word study to discuss the effect of the different structures of the competitors on the equilibrium solution. Moreover, sensitivity analyses are conducted and managerial insights are offered.

In an environment where markets go through a volatile process, and rapid fundamental changes occur due to technological advances, it is important to ensure and maintain a good performance measurement. Organizations, in their performance evaluation, should consider different types of financial and non-financial indicators. In systems like direct sales stores in which decision units have multiple inputs and outputs, all criteria influencing on performance must be combined and examined in a system, simultaneously. The purpose of this study is to evaluate the performance of different products through direct sales of a firm named Shirin Asal with a combination of Balanced Scorecard, fuzzy AHP and TOPSIS so that the weaknesses of subjectivity and selective consideration of evaluators in evaluating the performance indicators are reduced and evaluation integration is provided by considering the contribution of each indicator and each indicator group of balanced scorecard. The research method of this case study is applied. The data collection method is a questionnaire from the previous studies, the use of experts' opinions and the study of documents in the organization. MATLAB and SPSS were used to analyze the data. During this study, the customer and financial perspectives are of the utmost importance to assess the company branches. Among the sub-criteria, the rate of new customer acquisition in the customer dimension and the net income to sales ratio in financial dimension are of the utmost importance.

Recently, urbanization has been expanded rapidly in the world and a number of metropolitan areas have been appeared with a population of more than 10 million people. Because of dense population in metropolitan and consequently increasing the delivery of goods and services, there has been a lot of problems including traffic congestion, air pollution, accidents and high energy consumption. This made some complexities in distribution of urban goods; Therefore, it is essential to provide creative solutions to overcome these complexities. City logistics models can be effective in solving these complexities. In this paper, concepts and definitions related to city logistics are explained to provide a mathematical model in order to design city logistics distribution network aim at minimizing response times. This objective is effective for goods and emergency services, especially in times of crisis and also for goods that are delivered as soon as possible. This is a three-level network and has been used in modeling of queuing theory. To validate the model, a numerical example has been established and results of the model have been explained using BARON solver in Gams software. Finally, conclusions and recommendations for future research are presented.

The mismatch between students’ anthropometric measures and school furniture dimensions have been investigated in many countries. In Iran, collegians spend at least a quarter of the day hours at university in the sitting position, so it is essential to evaluate furniture mismatch among university students. In Iranian universities, the use of chairs with an attached table is widespread, while the study of mismatches in these chairs among the collegian community is rare. This study was aimed to compare and rank different classroom furniture types based on the mismatch between collegians’ anthropometric measures and the dimensions of classroom furniture among Industrial Engineering students by developing a Multi-Criteria Decision Making approach in an integrated Methodology. The sample consisted of 111 participants (71 males, 40 females). Ten anthropometric measures were gathered, together with eight furniture dimensions for four types of chairs. Mismatch analyses were carried out using mismatch equations, and the Simple Additive Weighting method was used as a base method to solve the decision-making problem. The results indicated that Underneath Desk Height and Seat to Desk Clearance showed the highest levels of the match, while Seat Width presents the highest levels of low mismatch. According to the results, Type 1 and Type 3 were the best current classroom furniture. The Sensitivity Analysis was performed in two ways: changing the weights of criteria in nine scenarios and comparing the results with five other MCDM methods. The proposed MCDM approach can be used widely in furniture procurement processes and educational environments.

Due to the rapid development of technology and emerging of severe competitive environments, the main objective of decentralized distribution networks operated by different providers is to maximize their profits in the network. One of the key tools to improve network performance in distribution networks is game theory and cooperative games. The main objective of this study is to investigate the role of small and medium industries in the development of the country's economic activity and to achieve a collaborative model to organize decentralized networks effectively. This makes these industries consider their main variables such as capacity, time and cost as important elements in today's competitive environments and try to increase their service levels to customers and promote the products according to customers' demand .On the other hand, in the real world due to various reasons such as sanctions, politics issues, earthquake, and war, time, the amount of flow, and cost are changed. Thus, to overcome unforeseen issues, it is important for suppliers to know how to cooperate to gain more payoffs in the network in order. In this study a mathematical problem for these industries is provided that considers various scenarios in uncertain situations. Then, to explain and demonstrate the applicability of the model, three numerical examples in these industries are discussed and the performances of cooperative and non-cooperative approaches are compared. The results show that cooperative games lead to an increase in players' payoffs and the utility function has boosted the effectiveness of cooperative.

In this research, a multi-objective mix integer programming for supply chain is proposed in which forward and reverse logistic is considered, simultaneously. In addition to cost, minimization of the environmental influences such as carbon dioxide emission and water consumption and also the social issues such as generated jobs and their equitable distributionis considered. Moreover, it is assumed that the demand is uncertain and the problem has been modeled using robust optimization. Proposed model determines the type of technology and capacity of facilities other than optimal location and flows between them. Eventually, the efficiency of robust model is investigated by applying it on steel industry as a case study.

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

Due to the increasing competition of globalization, selection of the most appropriate supplier is one of the key factors for asupply chain management’s success. Due to conflicting evaluations and insufficient information about the criteria, Intuitionisticfuzzy sets (IFSs) considered as animpressive tool and utilized to specify the relative importance of the criteria. The aim of this paper is to develop a new approach for solving the decision making processes. Thusan intuitionistic fuzzy multi-criteria group decision making approach is proposed. Interval-valued intuitionistic fuzzy ordered weighted aggregation (IIFOWA) is utilized to aggregate individual opinions of decision makers into a group opinion. A linear programming model is used to obtain the weights of the criteria.Then acombined approach based onGRAand TOPSIS method is introduced and applied to the ranking and selection of the alternatives. Finally a numerical example for supplier selection is given to illustrate the feasibility and effectiveness of the proposed method. A combined method based on GRA and TOPSIS associated with intuitionistic fuzzy set has enormous chance of success for multi-criteria decision-making problems due to containing vague perception of decision makers’ opinions. Therefore, in future, intuitionistic fuzzy set can be used for dealing with uncertainty in multi-criteria decision-making problems such as project selection, manufacturing systems, pattern recognition, medical diagnosis and many other areas of management decision problems.

Due to the particular importance of projects in human life and in organizations, proper project management has been always regarded highly by researchers and practitioners. Recent advances in technology and fundamental changes in most scientific areas have affected projects and made their nature and environmental circumstances much more complex than in the past. Fortunately, in recent years, many scholars have recognized the importance of complexity in modern project management and tried to identify its various aspects. Furthermore, one of the main factors for a project’s success is the assignment of an appropriate project manager. Many studies have been done about project managers' competencies and the selection methods of a suitable project manager. In most of these researches, the amount and type of project complexity have been explained as influential factors for determining the competent project manager. However, a specific approach for project manager selection considering the complexity of projects is not provided yet. Hence, in this paper we try to design and implement a fuzzy group decision making approach to allocate the best project manager taking into account the project complexity. Also, owing to the importance of construction projects in the development of countries' basic infrastructures, we exclusively studied this kind of projects. Finally, it should be noted that from the viewpoint of complexity theory, system complexity can exist in two forms: static and dynamic. Therefore, considering the breadth of issues related to each of these two complexity areas, just the static complexity of construction projects has been studied here.

ABSTRACT This paper studying the impact of strategic customer behavior on decentralized supply chain gains and decisions, which includes a supplier, and a monopoly firm as a retailer who sells a single product over a finite two periods of selling season. We consider three types of customers: myopic, strategic and low-value customers. The problem is formulated as a bi-level game where at the second level (e.g. horizontal game), the retailer determines his/her equilibrium pricing strategy in a non-cooperative simultaneous general game with strategic customers who choose equilibrium purchasing strategy to maximize their expected surplus. At the first level (e.g. vertical game), the supplier competes with the retailer as leader and follower in the Stackelberg game. They set the wholesale price and initial stocking capacity to maximize their profits. Finally, a numerical study is presented to demonstrate the impacts of strategic behavior on supply chain gain and decisions; subsequently the effects of market parameters on decision variables and total profitability of supply chain's members is studied through a sensitivity analysis.

In any supply chain, distribution planning of products is of great importance to managers. With effective and flexible distribution planning, mangers can increase the efficiency of time, place, and delivery utility of whole supply chain. In this paper, inventory routing problem (IRP) is applied to distribution planning of perishable products in a supply chain. The studied supply chain is composed of two levels a supplier and customers. Customers’ locations are geographically around the supplier location and their demands are uncertain and follow an independent probability distribution functions. The product has pre-determined fixed life and is to be distributed among customers via a fleet of homogenous vehicles. The supplier uses direct routes for delivering products to customers. The objective is to determine when to deliver to each customer, how much to deliver to them, and how to assign them to vehicle and routes. The mentioned problem is formulated and solved using a stochastic dynamic programming approach. Also, a numerical example is given to illustrate the applicability of proposed approach.

One of the primary concerns in investment planning is to determine the number of shares for asset with relatively high net value of share such as Berkshire Hathaway on Stock market. Traditional asset allocation methods like Markowitz theorem gives the solution as a percentage and this ratio may suggest allocation of half of a share on the market, which is impractical. Thus, it is necessary to propose a method to determine the number of shares for each asset. This paper presents a knapsack based portfolio selection model where the expected returns, prices, and budget are characterized by interval values. The study determines the priority and importance of each share in the proposed model by extracting the interval weights from an interval comparison matrix. The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. Finally, a discrete firefly algorithm is designed to find the near optional solutions in large dimensions. The proposed study is implemented for some data from the US stock exchange.

In the present paper, a novel intuitionistic fuzzy Multiple Attribute Decision Making (MADM) is proposed for modelling and solving analytical hierarchy process (AHP) problems with small amount of relationship among various criteria. Assigning a membership degree, fuzzy sets can model some uncertainty to the decision space. Intuitionistic fuzzy sets model the uncertainty more accurately associated with both membership and non-membership degree. Based on advantages of Intuitionistic fuzzy sets, this paper first uses IF-AHP to evaluate the weighting for each criterion and then develops an intuitionistic fuzzy DEMATEL method to establish contextual relationships among those criteria. Finally, an integrated IF-DEMATEL-AHP method is proposed and used for a case study for selecting managers in the automobile industry in Iran.

Product mix problem is one of the most important decisions made in production systems. Several algorithms have been developed to determine the product mix. Most of the previous works assume that all resources can perform, simultaneously and independently, which may lead to infeasibility of the schedule. In this paper, product mix problem and scheduling are considered, simultaneously. A new mixed-integer programming (MIP) model is proposed to formulate this problem. The proposed model differentiates between process batch size and transfer batch size. Therefore, it is possible to have overlapped operations. The numerical example is used to demonstrate the implementation of the proposed model. In addition, the proposed model is examined using some instances previously cited in the literature. The preliminary computational results show that the proposed model can generate higher performance than conventional product mix model.

The primary purpose of this paper is to highlight for the research community and practitioners the various aspects of using VMI-type supply chains in today’s business environment as well as a number of directions for future studies. In this regard, fifty articles published in major international journals, beginning in 1995, which contribute to the VMI-type supply chains are reviewed via a systematic review methodology. Our findings show there is an incremental growth in employing of VMI strategies in logistic and supply chains. This paper characterizes the design aspects required to configure and establish a VMI-type supply chain in the industry including demand pattern, number of products, contract type between two parties, and profit sharing scheme. Moreover, the current gaps on the current state of VMI-type supply chain in literature are highlighted in last section of this paper that may motivate future studies.

Optimal pricing and marketing planning plays an essential role in production decisions on deteriorating items. This paper presents a mathematical model for a three-level supply chain, which includes one producer, one distributor and one retailer. The proposed study considers the production of a deteriorating item where demand is influenced by price, marketing expenditure, quality of product and after-sales service expenditures. The proposed model is formulated as a geometric programming with 5 degrees of difficulty and the problem is solved using the recent advances in optimization techniques. The study is supported by several numerical examples and sensitivity analysis is performed to analyze the effects of the changes in different parameters on the optimal solution. The preliminary results indicate that with the change in parameters influencing on demand, inventory holding, inventory deteriorating and set-up costs change and also significantly affect total revenue.

One of the primary concerns with data envelopment analysis arises when the number of inputs and outputs increases. In such a case, required computations will become time consuming and there is a concern on developing some hybrid methods to increase the capability of this method. In this paper, a combination of data envelopment analysis and multi-layer neural network is proposed, in a case study, for predicting the performance of Alborz insurance subsidiaries in large cities in Automobile insurance firms. This research can be used in the future databases for performance prediction. On the other side, these results can aid Alborz insurance in its future investments as well as the performance index usage.

Several researchers have considered similarities between Multi-Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA), as tools for solving decision making problems. As the preferences of decision- maker (DM) on alternatives are not considered in classical DEA, some researchers have tried to consider it in DEA. The UTA-STAR method is one of the techniques widely used in Multi Criteria Decision Analysis. In this technique, the preferences of decision maker on alternatives are considered and UTA-STAR tries to compute the most suitable weights for criteria and alternatives to obtain a utility function having a minimum deviation from the preferences. The goal of this paper is interpreting decision maker’s preferences in UTA-STAR method, in a new manner, using the common set of weights (CSW) in DEA.

The Electre III is a widely accepted multi attribute decision making model, which takes into account the uncertainty and vagueness. Uncertainty concept in Electre III is introduced by indifference, preference and veto thresholds, but sometimes determining their accurate values can be very hard. In this paper we represent the values of performance matrix as interval numbers and we define the links between interval numbers and concordance matrix .Without changing the concept of concordance, in our propose concept, Electre III is usable in decision making problems with interval numbers.

Uncertainty plays an important role on many engineering problems and there is a growing interest in having reliable solutions especially for problems with sensitive parameters. The paper presents a robust optimization (RO) model for multi-objective operation of capacitated P-hub location problems (MCpHLP) under uncertainty set. There are, at least, two parameters in any P-hub problems, which are under uncertainty. The first one is associated with demand and the second one is the amount of time required to process commodities. We present a scenario based robust optimization technique, where these two items are considered under various scenario and a RO is implemented to find reliable solutions. The implementation of the proposed RO model is demonstrated for an example using weighting method.

Fuzzy Logic is one of the concepts that has created different scientific attitudes by entering into various professional fields nowadays and in some cases has made remarkable effects on the results of the practical researches. However, the existence of stochastic and uncertain situations in risk and accident field, affects the possibility of the forecasting and preventing the occurrence of the accident and the undesired results of it.In this paper, fuzzy approach is used for risk evaluating and forecasting, in accidents caused by working with vehicles such as lift truck. Basically, by using fuzzy rules in forecasting various accident scenarios, considering all input variables of research problem, the uncertainty space in the research subject is reduced to the possible minimum state and a better capability of accident forecasting is created in comparison to the classic two-valued situations. This new approach helps the senior managers make decisions in risk and accident management with stronger scientific support and more reliably.

Due to limited organizational budget, priority and performance of suitable technology strategies to eliminate or reduce technology gaps has got high importance. Ranking and proper choice of present and newfangled technologies needed by an organization for performance will elevate competitive privileges of the organization. In common methods first specific strategies, then all are ranked and executed according to the limitations of organization resources. In that case it is possible to choose homogeneous strategies which will raise unbalanced growth of the organization. In this paper, firstly the condition of present technology of organization is evaluated through CAPTECH method and proper strategies are defined. Contrary to common methods, introducing and using a novel heuristic method, strategies are classified in several clusters. Secondly, strategies within each cluster are ranked by TOPSIS method and at the time of execution a top strategy is chosen from each cluster. Thereupon, the efficiency of applying the strategies and possibility of balanced growth of organization will dramatically increase. To evaluate the proposed method, a case study has been done in a cold storage facility and the positive results have been demonstrated.

In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage, multi-product logistics network design is proposed by formulating a generalized logistics network problem into a mixed-integer nonlinear programming model (MINLP) for minimizing the total cost of the closed-loop supply chain network. Moreover, the proposed model is solved by using optimization solver, which provides the decisions related to the facility location problem, optimum quantity of shipped product, and facility capacity. Numerical results show the power of the proposed MINLP model to avoid th sub-optimality caused by separate design of forward and reverse logistics networks and to handle various transportation modes and periodic demand.

One of the primary managerial decisions for manufacturing units is to find out which activity must be outsourced. A good outsourcing decision is normally involved with different criteria such as opportunity costs, cost saving, etc. In this paper, we present a multi criteria decision-making method to find a suitable solution for outsourcing activities called preference ranking organization method for enrichment evaluations (PROMETHEE). The proposed model of this paper uses fuzzy numbers to determine the relative importance of different criteria and it is implemented for a real-world case study of aerospace industry.

Nowadays electronic commerce plays an important role in many business activities, operations, and transaction processing. The recent advances on e-businesses have created tremendous opportunities to increase profitability. This paper presents a multi-objective marketing planning model which simultaneously determines efficient marketing expenditure, service cost and product's selling price in two competitive markets. To solve the proposed model, we discuss a multi-objective geometric programming (GP) approach based on compromise programming method. Since our proposed model is a signomial GP and global optimality is not guaranteed for the problem, we transform the model to posynomial form. Finally, the solution procedure is illustrated via a numerical example and a sensitivity analysis is presented.

Supplier selection, Multi-objective decision making, Fuzzy Compromise programming, Supply chain management, Quantity discount . Supplier selection is naturally a complex multi-objective problem including both quantitative and qualitative factors. This paper deals with this issue from a new view point. A quantity discount situation, which plays a role of motivator for buyer, is considered. Moreover, in order to find a reasonable compromise solution for this problem, at first a multi-objective modeling is presented. Then a proposed fuzzy compromise programming is utilized to determine marginal utility function for each criterion. Also, group decision makers’ preferences have taken into account and the weight of each criterion has been measured by forming pair-wise comparison matrixes. Finally the proposed approach is conducted for a numerical example and its efficacy and efficiency are verified via this section. The results indicate that the proposed method expedites the generation of compromise solution .