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The redundancy allocation problem (RAP) is one of the most commonly used approaches for improving the reliability of systems. In previous studies, to simplify the problem model and mathematical solution methods, some real conditions were not emphasized in analysing the issue of RAP. But the present study, while considering these conditions, enables considering multiple goals, analysing repairable systems and examining the failure rate and repair rate appropriate to the type of components. Two goals of this purpose are maximizing the Mean Time To First Failure (MTTFF) and minimizing the total cost of the system, considering the weight and volume limitations and number of plugin components. To solve the problem model four Multi-objective meta-heuristic algorithms have been used: NSGA-II, MOPSO, PESA-II and MALO. In addition, the simulation method is used to estimate the first objective function of the problem. [ABSTRACT FROM AUTHOR]

Modern technologies have changed human life and created a generation of customers who have different needs compared to the past. Considering Industry 4.0 and its drivers, the implementation of digital banking (DB) has faced various challenges that are caused by emerging trends. Both Industry 4.0 and DB are contemporary concepts, and decision-makers are often faced with uncertainties in their decisions regarding the implementation of DB and its indicators. For this purpose, a novel multi-criteria group decision-making approach has been developed utilizing the best–worst method (BWM) and α-cut analysis as well as trapezoidal fuzzy numbers (TFNs). By reviewing the literature and using experts' opinions, the DB implementation criteria are determined, and considering an uncertain environment, the criteria are prioritized using the proposed method. Then, the available DB models and alternatives are examined based on the decision criteria and the importance of each criterion. This research contributes to the existing literature by identifying and prioritizing the criteria necessary for the successful implementation of DB, taking into account emerging trends and technological advances driven by Industry 4.0. Subsequently, the study prioritizes the prevalent models of DB based on these criteria. This study proposes a decision-support framework for dealing with ambiguity, lack of information, insufficient knowledge, and uncertainty in decision-making. The framework uses TFNs to account for imprecision and doubt in decision-makers' preferences. Additionally, the study presents a fuzzy multi-criteria group decision-making approach that enables a group of experts to arrive at more reliable results. The proposed approach can help improve the quality of decision-making in complex and uncertain situations. The results of this research show that human resources, rules and regulations, and customer satisfaction are the most important criteria for implementing DB. In addition, the open, blockchain, and social banking models are the crucial models that significantly cover the implementation criteria for DB. [ABSTRACT FROM AUTHOR]

This study proposes a new model to optimize sales promotion in competitive markets and examines the impact of competition on sales promotion planning and business performance in retail chains. The model can be used to determine the best promotional discount for different products with a cannibalization effect when competitors are present in the retail market and offer the same products with different discounts. An integer nonlinear programming problem is proposed to model the above issue. To solve the model, it is reformulated as a mixed-integer linear programming problem. Consequently, a MIP solver can be used to solve the model in a reasonable CPU time. Several examples are solved and a sensitivity analysis of the model parameters is performed. The results of our numerical study show interesting findings that considering different competitors is very important in promotion planning and optimization. Failure to take them into account can lead to loss of profits. [ABSTRACT FROM AUTHOR]

Today, most of the issues and challenges faced by managers and decision makers are complex and multifaceted. More clearly, due to the developments of technologies, emerging trends in various industries, competitive markets, and rapid and transformative changes in the business environment, managers and decision makers have faced an uncertain environments and issues that cannot be resolved definitively. The use of multi-criteria decision-making (MCDM) methods as a practical and decision-supporting tool allows managers to examine decision-making issues in various organizations and industries based on various criteria, alternatives, and objectives and make decisions with greater reliability. The use of fuzzy techniques and concepts in MCDM methods and their mathematical relationships makes it possible to consider complexities and uncertainties in decisions related to various issues and it can lead to better and more realistic decisions. In this paper, the simplified best-worst method (SBWM), which is one of the methods based on pairwise comparisons, has been developed using triangular fuzzy numbers (TFNs) to propose a fuzzy extension of SBWM (F-SBWM). Triangular fuzzy numbers in different symmetric and asymmetric forms have widely been used in MCDM approaches and pairwise comparisons. It is noteworthy that symmetric numbers are used when we are using equal division of the domain due to an increased ambiguity and lack of information. The proposed approach as a simplified fuzzy MCDM method helps managers and decision makers in various industries to solve decision-making problems under uncertainty without the need for complex calculations, specialized skills, and software packages. To check the feasibility and applicability of the proposed approach, two numerical examples and a computational experiment with real data are presented, and the results are analyzed and discussed. Furthermore, to check the robustness of the results obtained from the proposed approach, sensitivity analysis and comparison of methods have been performed. [ABSTRACT FROM AUTHOR]

Renewable energy development is important in the current era due to the attention paid to environmental issues and energy demand growth. On the other hand, the growth and expansion of the use of RE requires cooperation and participation in the Renewable Energy Supply Chain (RESC). The strategic alliance between the parties involved in the field of RE can play an important role in reducing risk and significant growth in RESC. Also, due to various capabilities such as transparency, traceability, decentralization, security, and immutability of information, blockchain plays an important role in improving trust and strengthening cooperation in strategic alliances between partners in RESC. Therefore, this study proposes a decision-making framework under uncertainty for integrating blockchain and strategic alliance in RESC. The proposed framework involves the CRiteria Importance Through Intercriteria Correlation (CRITIC) and Fuzzy Combined Compromise Solution (F-CoCoSo) techniques. Also, validation and sensitivity analysis are performed using several fuzzy decision-making techniques. The findings indicated that matching energy supply and demand through information sharing on the blockchain platform and cooperating with companies in the RESC was recognized as the most important solution. • Integration of blockchain and strategic alliance in renewable energy supply chain. • Offering a decision framework using CRITIC-CoCoSo techniques to assess solutions. • Suggesting a robust approach based on several fuzzy MCDM methods in real-case. • Proposing a further application of blockchain in the renewable energy supply chain. • Presenting energy supply and demand match using blockchain as the best solution. [ABSTRACT FROM AUTHOR]

Studies related to transportation planning and development have been in the center of activities of many researchers in the past decades. Road congestions issues, economic problems, health problems and environmental problems are some examples of complex problems that can be caused by urban and public transportation in big cities. Evaluating urban and public transportation systems could help to reach effective solutions to overcome these issues. This article presents a short bibliographic review of some recent studies on Multi-Criteria Decision-Making (MCDM) approaches for evaluating urban and public transportation systems. To this aim, Scopus was chosen as the database for making a search on journal articles. Scopus is trusted by major institutions in the world, and all journals covered in this database are inspected for sufficiently high quality each year. The search was made on the journal articles from 2017 to 2022 (July). The analyses presented in this study show that the Analytic Hierarchy Process (AHP) method is the most used method, which has been applied to different studies in the field of urban and public transportation systems based on MCDM approaches. According to the analysis of the number of articles, Turkey is ranked 1st among different countries, and "Budapest University of Technology and Economics" (Hungary) is 1st in the ranking of institutions. Moreover, most of the articles have been published within the "social sciences" subject area. The recent trend in different studies on urban and public transportation systems shows the importance of using MCDM approaches in this field. Moreover, noticeable employment of fuzzy sets in several studies is a point that can shows the significant role of uncertainty in dealing with this type of problems. [ABSTRACT FROM AUTHOR]

Production-routing problem (PRP) integrates different decisions along the supply chain simultaneously. Studies on PRP only determine the amount and time of production in a linear and continuous production system. While for the supply chain with scattered and different customers, it is necessary to have a flexible production system such as a cellular manufacturing system (CMS). The proposed model incorporates the cell formation problem (CFP) into a PRP. This designing problem in CMS determines the allocation of machines and products to cells and reduces the cost of intercellular movements. We apply a Robust credibility-based fuzzy programming (RCFP) approach versus the possibilistic chance-constrained programming (PCCP) for demand uncertainty. We optimize the model by Teaching-Learning-based Optimization (TLBO) and simulated annealing (SA) algorithms. The convergence in results shows the effectiveness of both algorithms, especially the TLBO algorithm. Furthermore, RCFP leads to a more robust solution with the intelligent choice of the confidence level for satisfying chance constraints and less sensitivity to the changes in non-deterministic parameters. [ABSTRACT FROM AUTHOR]

Due to the growth in energy demand and the need to pay attention to sustainable development, many developing countries face the challenge of making policies for growing sustainable energy. With attention to industrial expansion and population growth, Iran, a developing country, is confronting an energy shortage problem. Despite the appropriate capacity of renewable resources in Iran, due to the lack of proper planning for adopting renewable resources in Iran, these resources have a small share of the energy supply. This study aims to present a decision framework for assessing renewable energies in developing countries, in the case of Iran. In addition to technical aspects, sustainability aspects such as economic, social, and environmental factors are proposed to assess renewable sources in this work. The Best-Worst Method (BWM) and the Interval-Valued Pythagorean Fuzzy Weighted Aggregated Sum Product Assessment (IVPF-WASPAS) techniques are suggested to prioritize renewable sources. The findings indicated that solar energy was Iran's most suitable renewable resource. Also, the validation and sensitivity analysis confirmed the findings. This research provides a comprehensive perspective for policy-making and planning of sustainability in the energy industry. • Proposing a robust hybrid decision approach to assess renewable energy projects. • Considering sustainability aspects in evaluating renewable energy projects. • Presenting a new application of the interval-valued Pythagorean fuzzy WASPAS. • Introducing solar energy as the most suitable source to meet Iran's energy demand. • Specifying the most suitable renewable energy in different regions of Iran. [ABSTRACT FROM AUTHOR]

The best-worst method (BWM) is a multi-criteria decision-making method which works based on a pairwise comparison system. Using such a systematic pairwise comparison enhances consistency and reliability of results. The BWMresults in single solution when there are two or three criteria, and for problems with fully-consistent systems, with any number of criteria. To obtain the weights of criteria for not fully-consistent comparison systems with more than three criteria, there may be a multiple optimal solution. Although multiple optimality may be desirable in some cases, in other cases, decision-makers prefer to have a unique optimal solution. This study proposes new models which result in a unique solution. The proposed models have less constraints in comparison with the previous models. [ABSTRACT FROM AUTHOR]

Nowadays, due to the increase in energy demand, the adverse effects of fossil fuels, and the attention to sustainability issues, the development and use of Renewable Energy (RE) has expanded worldwide. On the other hand, RE for energy supply is also faced with challenges due to obstacles and risks. New technologies can reduce barriers and the risk of energy supply from RE. One of these technologies is the blockchain, which can solve many RE development risks. As a result, this research aims to find and assess blockchain strategies for reducing RE development risk. A novel decision framework, including SWOT analysis and grey MCDM approaches, is proposed in this regard. SWOT analysis is utilized to determine strategies. After conducting a thorough investigation, we have proposed using Grey Step-wise Weight Assessment Ratio Analysis (SWARA-G) and Grey Combined Compromise Solution (CoCoSo-G) approaches for ranking strategies. Also, to check the accuracy of the results, sensitivity analysis, and validation are performed with several MCDM techniques. The findings showed that reforming the energy industry and eliminating monopoly can be realized by creating a decentralized energy system based on blockchain technology to reduce the cost and price of RE production. • Proposing a decision framework to grow blockchain-based renewable energy. • Offering a novel integrated SWARA-CoCoSo framework using grey theory. • Integration of SWOT analysis and hybrid MCDM methods in the proposed framework. • Considering the risks and uncertainty in blockchain-based renewable energy in Iran. • Offering a blockchain-decentralized system to reduce the cost of renewable energy. [ABSTRACT FROM AUTHOR]

• Identifying blockchain solutions to gain sustainability in the supply chains. • Offering a new application for using blockchain in the agricultural products. • Presenting a novel integrated SWARA-CoCoSo approach based on Z-numbers. • Evaluating sensitivity analysis and validation of the proposed approach results. • Providing flexible, robust, and reliable results using the developed framework. Nowadays, because of the increase in global competition and the need to pay attention to sustainable issues, achieve a competitive advantage, reduce costs, and improve Supply Chain (SC) network management, the use of blockchain and its integration have become necessary in the network. The use of Blockchain Technology (BT) leads to transparency, controllability, security, flexibility, visibility, reduction of costs, efficient processes, and achieving sustainable development goals in the SC network. To successfully implement BT in SC, it is necessary to identify and evaluate the operational solutions to solve the challenges and obstacles of BT. Therefore, this study aims to identify and evaluate BT implementation solutions to achieve sustainability in the SC. The novelties and contributions of this research include identifying blockchain solutions to achieve sustainable development goals in the SC, proposing a new application for the agricultural products SC to utilize blockchain, and presenting a novel hybrid decision procedure including the Step-wise Weight Assessment Ratio Analysis (SWARA) and the Combined Compromise Solution (CoCoSo) using on Z-numbers for evaluating solutions. The proposed approach can deal with ambiguity in the complex environment of the decision due to the consideration of reliability and uncertainty in the data, reducing the number of pairwise comparisons, low inconsistency rate, reducing the complexity of calculations, ease of implementation, no limit in the number of criteria, no dependence on predetermined scales, and providing compromise solutions based on different opinions of experts. To examine the results, sensitivity analysis, and validation were performed. The findings showed that for the successful implementation of blockchain in the agricultural SC, the infrastructure of this technology must be created and integrated with all levels of the SC, and using BT must be provided through the use of sustainable methods. By developing areas of cooperation and participation through the creation of facilitating rules between different partners in the SC, collective investments can be made in the development of the infrastructure. [ABSTRACT FROM AUTHOR]

Using the Internet of Things (IoT) and its integration into Renewable Energy Systems (RES) leads to energy planning, management of energy consumption and demand, and, as a result, sustainably matches energy supply and demand. There are several risks in implementing and integrating IoT in RES, which is an obstacle to successfully implementing these systems. Therefore, this research aims to assess the risks of implementing IoT in RES based on the hybrid Multi-Criteria Decision-Making (MCDM) approach under uncertain conditions in Iran. In this study, to specify the importance of criteria, the Fuzzy Stepwise Weight Assessment Ratio Analysis (F-SWARA) method is utilized, and to assess the risks of IoT integration in RES, the usage of the Fuzzy Combined Compromise Solution (F-CoCoSo) method is suggested. The following examines the sensitivity analysis and validation with fuzzy WASPAS, fuzzy ARAS, and fuzzy TOPSIS techniques. The findings showed that the proposed approach had adequate robustness and flexibility for risk assessment, and risks of investment, infrastructure development, and privacy and security were introduced as the most critical risks. • Proposing the integration of the Internet of Things in renewable energy. • Assessing the integration risks of the Internet of Things in renewable energy. • Offering a fuzzy decision framework of SWARA and CoCoSo for ranking risks. • Presenting sensitivity analysis and validation using several MCDM methods. • Evaluating the application of the proposed framework for integrating IoT in Iran. [ABSTRACT FROM AUTHOR]

In this paper, a mixed-integer nonlinear programming model is developed for a general edible oil closed loop supply chain network design problem under hybrid uncertainty which is then transformed to its linear counterpart. In order to cope with the hybrid uncertainty in input parameters, scenario-based and fuzzy- based parameters, a new approach is proposed including a novel robust fuzzy programming and an efficient method based on the Me measure. Furthermore, the performance of the proposed model is compared with that of other models. Finally, numerical studies and simulation are performed to verify our mathematical formulation and demonstrate the benefits of the proposed model. [ABSTRACT FROM AUTHOR]

In the discrete form of multi-criteria decision-making (MCDM) problems, we are usually confronted with a decision-matrix formed from the information of some alternatives on some criteria. In this study, a new method is proposed for simultaneous evaluation of criteria and alternatives (SECA) in an MCDM problem. For making this type of evaluation, a multi-objective non-linear programming model is formulated. The model is based on maximization of the overall performance of alternatives with consideration of the variation information of decision-matrix within and between criteria. The standard deviation is used to measure the within-criterion, and the correlation is utilized to consider the between-criterion variation information. By solving the multi-objective model, we can determine the overall performance scores of alternatives and the objective weights of criteria simultaneously. To validate the proposed method, a numerical example is used, and three analyses are made. Firstly, we analyse the objective weights determined by the method, secondly, the stability of the performance scores and ranking results are examined, and finally, the ranking results of the proposed method are compared with those of some existing MCDM methods. The results of the analyses show that the proposed method is efficient to deal with MCDM problems. [ABSTRACT FROM AUTHOR]

Abstract: Redundancy allocation is one of the adopted approaches that is used by system designers to improve the performance of systems. In this article, a new model and a novel‐solving method are provided to address the nonexponential redundancy allocation problem in series‐parallel systems with repairable components based on optimization via simulation approach and artificial neural network technique. Despite the previous researches, in this model the failure and repair times of the each component were considered to have nonnegative exponential distributions. This assumption makes the model closer to the reality where most of used components have greater chance to face a breakdown in comparison to new ones. The main aim of this research is the optimization of mean time to the first failure of the system via allocating the best redundant components for each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique and artificial neural network were applied to model the problem, and different experimental designs were produced using design of experiment methods. To solve the problem, some metaheuristic algorithms were integrated with the simulation method. Several experiments were performed to test the proposed approach, and as the results show, the proposed approach is much more real than previous models, and also the near optimum solutions are promising. [ABSTRACT FROM AUTHOR]

Selection of appropriate subcontractors for outsourcing is very important for the success of construction projects. This can improve the overall quality of projects and promote the qualification and reputation of the main contractors. The evaluation of subcontractors can be made by some experts or decision-makers with respect to some criteria. If this process is done in different time periods, it can be defined as a dynamic multi-criteria group decision-making (MCGDM) problem. In this study, we propose a new fuzzy dynamic MCGDM approach based on the EDAS (Evaluation based on Distance from Average Solution) method for subcontractor evaluation. In the procedure of the proposed approach, the sets of alternatives, criteria and decision-makers can be changed at different time periods. Also, the proposed approach gives more weight to newer decision information for aggregating the overall performance of alternatives. A numerical example is used to illustrate the proposed approach and show the application of it in subcontractor evaluation. The results demonstrate that the proposed approach is efficient and useful in real-world decision-making problems. [ABSTRACT FROM AUTHOR]

Because of the possible harmful effects of construction equipment on the environment, evaluation of them can be considered as a helpful activity to move toward the sustainability in construction. This evaluation process could involve some alternatives and some criteria in a discrete decision space. In this study, a new hybrid multi-criteria decision-making (MCDM) approach is proposed to deal with this evaluation process in the fuzzy environment. We present fuzzy extensions of the SWARA (Step-wise Weight Assessment Ratio Analysis) and CRITIC (CRiteria Importance Through Intercriteria Correlation) methods for determining subjective and objective weights of criteria. Based on these extended methods and the fuzzy EDAS (Evaluation based on Distance from Average Solution) method, a new hybrid approach is proposed. In this approach, the subjective and objective criteria weights are combined to determine more justified weights for criteria. The proposed approach is applied to a case study of construction equipment evaluation with sustainability considerations. To examine the result of evaluation, a sensitivity analysis is performed based on varying criteria weights. A comparison is also made between the results of the proposed approach and some existing MCDM methods. These analyses show the stability and validity of the results and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

• Proposing the defuzzification relations of equality constraints with Nec measure. • Offering a new fuzzy network slack based model on the basis of necessity measure. • The proposed relations combine the Nec equality constraints and network SBM. • Using agile supply chain theory to validate the proposed approach. With respect to the necessity (Nec) measure, suggesting by Dubois and Prade, there is a dearth of any procedure or formula to deal with equality chance constraints. Therefore, this study attempted to introduce (Nec) and possibility (Pos) equality constraints in a real-case network non-oriented slack based model problem via agile supply chain (ASC) theory. The efficiency of different levels of agility of a dairy supply chain (SC), using a new fuzzy network Date Envelopment Analysis slack based model (FNDEA-SBM) on the basis of (Pos)/(Nec) measures, has been measured in this paper. The measurement took place in a multi-stage process. Since the agility features were rather qualitative in nature, the measures were likely to be delineated subjectively using linguistic expressions and fuzzy data. The application of (Pos)/(Nec) measures and different confidence levels made decision makers capable of getting information of all efficiency and sub efficiency scores through optimistic-pessimistic lenses and taking appropriate decisions on the basis of different optimistic-pessimistic points of view and confidence levels. The validity of the suggested approach was demonstrated through a real-life case study related to Iranian top twenty dairy firms. The results indicated the success of the suggested approach and computations were straightforward. [ABSTRACT FROM AUTHOR]

Nowadays, attention to the goals of cost, quality, time, and risk is essential in every project. In this regard, the beneficiaries of each project seek to reduce the cost, time, and risk and increase the quality of the project simultaneously. As cost, risk, time, and quality are critical factors in project management, this study primarily focused on examining the trade-offs among these criteria to determine how increased costs and decreased risks can lead to reduced project implementation time and improved quality. To this end, in this research, a mathematical programming approach and metaheuristic algorithms have been suggested for PERT networks. The model is developed through a simulation process, and then the values of decision and response variables are obtained in each implementation. Finally, an artificial neural network model is designed, and several sample problems are generated on small, medium, and large scales to solve the model. This study has several contributions, including the development of a mathematical programming model for PERT networks that considers the cost–risk–time–quality trade-off. Additionally, an artificial neural network model is proposed for solving problems of various sizes. At the same time, NSGA-II and MOPSO algorithms are utilized to handle NP-hard problems in the mathematical programming of PERT networks. Furthermore, the model considers multi-dimensional activities, with cost, quality, risk, and time as the four objective functions in the PERT network. Finally, AHP–TOPSIS methods are used to evaluate the effectiveness of NSGA-II and MOPSO algorithms. The findings illustrated that the NSGA-II algorithm performed better than the MOPSO algorithm in all three designed problems. The proposed model became practical and closer to reality than the previous models by eliminating unrealistic assumptions. [ABSTRACT FROM AUTHOR]

Discrete stochastic multi-criteria decision-making (MCDM) can be used to handle many real-life decision-making problems. The Evaluation Based on Distance from Average Solution (EDAS) is a new and efficient MCDM method. The desirability of alternatives in this method is determined based on distances of them from an average solution. Because the average solution is determined by an arithmetic mean in this method, the EDAS method can be efficient for solving stochastic problems. In this paper, a stochastic EDAS method is proposed to handle problems in which the performance values of alternatives on each criterion follow the normal distribution. Based on the proposed method, we can obtain optimistic and pessimistic appraisal scores for evaluation of alternatives and consider the uncertainty of decision-making data. We present a graphical example to illustrate the proposed method and a practical example of performance evaluation of bank branches to show the applicability of it. According to the analyses made, the proposed method is efficient and the results are valid. [ABSTRACT FROM AUTHOR]

Integration of reverse logistics processes into supply chain network design can help to achieve a network that incorporates environmental factors as well as economic factors. In this study, a new integrated approach is proposed to address designing a multi-product, multi-period supply chain network with reverse logistics. The framework of the proposed approach includes green supplier evaluation and a mathematical model in an uncertain environment. To the best of our knowledge, integration of green supplier evaluation into the designing supply chain network with reverse logistics has not been considered in the literature. This integration can help to incorporate experts’ opinions about environmental impact of suppliers in the network design. Minimization of total cost and maximization of total greenness score of purchased raw materials/components are two objectives of the model. The fuzzy EDAS method is used to determine the greenness scores of suppliers. Also, demand of customers and capacity of suppliers are defined using fuzzy numbers and a fuzzy method is used to obtain trade-off solutions. The proposed approach is applied to designing the supply chain network of a home appliance company. The results show that the proposed approach is feasible and efficient to obtain solutions to design the supply chain network. [ABSTRACT FROM AUTHOR]

Many Studies have been conducted on Redundancy Allocation Problem (RAP), but only a few of them have considered designing systems which operate for a certain period of time. Such temporary systems are not meant to operate for a long period of time (e.g. a manufacturing cell designed to produce a unique product with a small window of opportunity). Due to this fact, the investigation of the reliability at an infinite time will not be helpful; instead designing a system which perform optimally in a short period of time is of importance. additionally RAP's are inherently complex and classified as NP-Hard, as a result most proposed approaches, consider a set of simplified assumptions. Among these assumptions are the calculation of availability in the steady state (a time in which a system becomes completely stable), the use of non-repairable components, and setting predetermined configuration strategy (parallel, cold or warm standby subsystems). Unfortunately, these simplified assumptions do not conform to the real world conditions. Therefore, this article intends to develop an integrated algorithm to solve the reliability design problem considering instantaneous availability, repairable components, and the selection of configuration strategies based on the Markov processes and NSGA-II algorithm. [ABSTRACT FROM AUTHOR]

The assessment of Third-Party Logistics (3PL) provider becomes an important issue for enterprises trying to achieve operational efficiency and customer service improvement as well as capital expenditure and logistics costs reduction. It can be said that evaluation and selection of an appropriate 3PL provider is a kind of Multi-Criteria Decision-Making (MCDM) problem. Uncertainty is an unavoidable part of information in the decision-making process. Interval Type-2 Fuzzy Sets (IT2FSs) are very flexible to model the uncertainty of the MCDM problems. In this study, a new integrated approach based on the CRiteria Importance Through Inter-criteria Correlation (CRITIC) and Weighted Aggregated Sum Product ASsessment (WASPAS) methods is proposed to evaluate 3PL providers with IT2FSs. In the proposed approach, objective weights resulted from the CRITIC method are combined with subjective weights expressed by decision-makers (DMs) to determine more realistic weights for criteria. A computational study is performed to illustrate the proposed approach and the applicability of it. In addition, a sensitivity analysis is carried out using different sets of criteria weights to demonstrate the stability of the proposed approach. The results show the stability of ranking results and prove the efficiency of the proposed approach to handle MCDM problems with IT2FSs. [ABSTRACT FROM AUTHOR]

The redundancy allocation problem (RAP) has been studied for many different system structures, objective functions, and distribution assumptions. In this paper, we present a problem formulation and a solution methodology to maximize the system steady-state availability and minimize the system cost for the repairable series-parallel system designs. In the proposed approach, the components' time-to-failure (TTF) and time-to-repair (TTR) can follow any distribution such as the Gamma, Normal, Weibull, etc. We estimate an approximation of the steady-state availability of each subsystem in the series-parallel system with an individual meta-model. Design of experiment (DOE), simulation and the stepwise regression are used to build these meta-models. Face centred design, which is a type of central composite design is used to design experiments. According to a max-min approach, obtained meta-models are utilized for modelling the problem alongside the cost function of the system. We use the augmented "-constraint method to reformulate the problem and solve the model. An illustrative example which uses the Gamma distribution for TTF and TTR is explained to represent the performance of the proposed approach. The results of the example show that the proposed approach has a good performance to obtain Pareto (near-Pareto) optimal solutions (system configurations). [ABSTRACT FROM AUTHOR]

• Identifying the effective criteria in stock portfolio selection; • Developing the SGBWM method for group decision-making; • Developing the FMCSM method for multi-criteria sorting problems; • Using an integrated approach to a real stock portfolio selection problem. Many real-life issues can be modeled as multi-criteria sorting problems. In this type of problem, a set of options are assigned to predefined classes. This research aims to propose a new hybrid approach including simplified group best–worst and multi-criteria sorting methods to classify options considering decision-maker constraints. It is crucial to consider such constraints, and ignoring them can make the results ineffective for the decision-maker. The method developed in this study for weighting the criteria can be considered the most unmixed group best–worst method ever proposed, as no optimization model and specialized software are needed to solve it. In this research, a multi-criteria sorting method is also suggested. The developed method compares alternatives with limiting profiles and then classifies alternatives based on their distance from the limiting profiles. This method can consider investor preferences, such as the number of alternatives in categories or the maximum number of stocks allowed from each industry in the stock portfolio. The proposed approach is used in a real case study of stock portfolio selection in the Tehran Stock Exchange. The results of the methods proposed in this study were compared with the results of the previous methods using numerical examples and a real case study. The results show that the developed methods are valid and accurate. The suggested approach helps managers to include their constraints in the decision-making process and can be applied to real-world problems with a classification nature. [ABSTRACT FROM AUTHOR]

It is highly crucial to design a supply chain in a way that determines to what extent each level should direct orders to the next level and, in cases where several next levels exist, which next level should be selected for such transmission of orders. The main significance of the proposed model is to choose an appropriate region for order placement and also investigate when a level in a specific region is disrupted what happens to the other levels in other regions. Also the main impact of this article is pertaining to managerial decisions that if a level in supply chain is shut down and disrupted what they should do Therefore, in the present study, system dynamics approach is used to design a five-level supply chain consisting of retailer, final product distributor, manufacturer, material distributor, and supplier in four different regions by specifying the amount of orders placed by each level at each region as well as the next level at each region that should receive these orders. The developed model is kind of multi-product model in which initial customer demand for any product is a random variable. There is only one entity in each region at every level, except for the number of material distributor(s) and supplier(s) which equals the number of raw material item(s). Finally, a numerical example is presented to discuss the result for normal conditions, oscillating demands, variations in price, changes in costs, and a combination of these variations which may vary depending on changes in the how orders are placed. [ABSTRACT FROM AUTHOR]

The reliability of flip-chip packages is significantly impacted by the type of packaging materials, such as underfill, solder, flux, and solder mask. Underfill reduces the coefficient of thermal expansion (CTE) mismatch between the semiconductor chip and printed circuit board, thereby minimizing the chances of thermal fatigue failure of the package. There are a plethora of underfill suppliers and selecting the most suitable underfill for preparing a flip-chip package is critical as there are conflicting criteria. These criteria are limited to glass transition temperature (Tg), CTE, elastic modulus (E), coefficient of moisture expansion, fracture toughness (Kic), shear strength, and flowability. Choosing appropriate alternatives based on the above conflicting criteria is basically a multi-criteria problem. This research proposes a grey simplified best–worst method (GSBWM) to identify the criteria weight. The proposed method has less calculation complexity than the previous grey best–worst methods, and it does not need operations research and modeling knowledge, and optimization software to solve it. The accuracy of the GSBWM is investigated using three data sets. The results showed the high accuracy of the proposed method in all data sets. This study employed the combination of GSBWM and a grey possibility degree based method to select the most suitable underfill material for reliable flip-chip packages as a real-world problem. [ABSTRACT FROM AUTHOR]

• Offering a novel fuzzy robust approach for closed-loop supply chain network design. • Examining the hybrid uncertainties and flexibility of constraints in the problem. • Considering economic, responsibility, and environmental subjects in modeling. • Using the interactive fuzzy programming approach to solve the multi-objective model. • Introducing a new application for stone paper closed-loop supply chain network design. Nowadays, the production of stone paper, in addition to its widespread utilization in various fields, does not require water consumption, cutting down trees, and stone paper products are easily recyclable and recoverable. Due to the importance of developing and using stone paper and paying attention to environmental matters, Closed-Loop Supply Chain Network Design (CLSCND) is very important for stone paper products. Moreover, due to epistemic, randomness uncertainties, the uncertainties in Objective Function (OF), and the flexible constraints for CLSCND in the real world, this paper introduces a novel Mixed Robust Stochastic, Possibilistic, and Flexible Programming (MRSPFP) approach based on credibility theory. The different attitudes of the Decision-Makers (DMs) are addressed by a more flexible measurement of the optimistic and pessimistic parameters using the criterion of credibility. In the study, a comprehensive procedure is proposed for stone paper CLSCND, to minimize costs, increase responsiveness by minimizing transit time between different facilities, and regarding environmental concerns into account through minimizing carbon emissions. The model is solved utilizing an interactive fuzzy programming solution procedure and the Best-Worst Method (BWM). The results of sensitivity analysis, the effect of changing the problem parameters, and the performance of the proposed model are investigated and compared. The results show that the MRSPFP model has a better performance compared to other previous models. The MRSPFP model performs better in strategic decisions that require high investment costs due to the minimization of the absolute deviation of OF from its mean. Also, the applied results of the study show that CLSCND has a good capability and potential for sustainable development in the field of stone paper. [ABSTRACT FROM AUTHOR]

Reliability optimization problem is an important type of optimization problems that has many practical applications in the real-world systems such as manufacturing systems, telecommunication systems, transformation systems and electrical systems. This research focuses on redundancy allocation problem (RAP) that is a special type of reliability optimization problems. A bi-objective RAP, which is related to a system of s independent k -out-of- n subsystems in series, is considered in this study. Maximization of the system reliability and minimization of the system cost are the objectives of the problem, and the system is constrained by a predefined weight. The components of a subsystem are supposed to be non identical. To deal with this problem, we propose some multi-objective meta-heuristic algorithms based on the elitist non-dominated sorting genetic algorithm (NSGA-II). New modified methods of diversity preservation and constraint handling are introduced in this study. According to these methods and some existing methods, we propose four multi-objective genetic algorithms for solving the considered problem. A numerical example, a statistical method and three performance metrics are utilized for analyzing and comparing the performance of these four genetic algorithms. The comparison represents the positive effect of modified methods of diversity preservation and constraint handling on the performance of the algorithms. [ABSTRACT FROM AUTHOR]

Supplier selection is one of the most critical activities of purchasing management in a supply chain because of the key role of supplier's performance in achieving the objectives of a supply chain. Supplier selection problem requires a trade-off between multiple criteria exhibiting vagueness and imprecision with the involvement of a group of experts. This paper presents a multiple criteria group decision-making approach for supplier selection problem in the context of interval type-2 fuzzy sets. A new method for ranking interval type-2 fuzzy numbers, based on the centroid of fuzzy sets, is proposed and compared with some methods. The proposed ranking method is used for extending complex proportional assessment (COPRAS) method for group decision-making with interval type-2 fuzzy numbers. The developed method uses a stepwise procedure for ranking and evaluating the alternatives, in terms of significance and utility degree, and selects the best solution considering both the positive-ideal and the negative-ideal solutions. To demonstrate the applicability of the proposed approach in supplier selection problems, an illustrative example is presented and the results are analyzed. [ABSTRACT FROM AUTHOR]

This paper considers a single machine scheduling problem, with the objective of minimizing a linear combination of total tardiness and waiting time variance in which the idle time is not allowed. Minimizing total tardiness is always regarded as one of the most significant performance criteria in practical systems to avoid penalty costs of tardiness, and waiting time variance is an important criterion in establishing quality of service (QoS) in many systems. Each of these criteria is known to be non-deterministic polynomial-time hard (NP-hard); therefore, the linear combination of them is NP-hard too. For this problem, we developed a genetic algorithm (GA) by applying its general structure that further improves the initial population, utilizing some of heuristic algorithms. The GA is shown experimentally to perform well by testing on various instances. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage. [ABSTRACT FROM AUTHOR]

Regression analysis is one of the most applicable methods in statistical methodology used to find the best regression model according to the relationship among several variables in a system. The estimation of regression model, which is solved as a formulate optimization problem and making use of heuristic algorithms, is much simpler and faster than classic methods. Genetic algorithm (GA) as one of the heuristic algorithms had been used to solve this problem. In this paper, we extend the noising method as a recent combinatorial optimization problem to estimate the best regression model and evaluate its performances compared to GA. Also, in order to enhance the performance of our GA, we apply the Taguchi experimental design method to tune the parameters of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto front of binary-state MORAP is generated using an efficient epsilon-constraint method. In order to test the quality of generated non-dominated solutions in this module, a multi-start partial bound enumeration algorithm is also proposed for MORAP. The performance of both procedures is compared using different metrics on well-known benchmark instance. The statistical analysis represents that not only the proposed efficient epsilon-constraint method outperform the multi-start partial bound enumeration algorithm but also it improves the founded upper bound of benchmark instance. Then, in second module, a DEA model is supplied to prune the generated non-dominated solutions of efficient epsilon-constraint method. This helps reduction of non-dominated solutions in a systematic manner and eases the decision making process for practical implementations. [Copyright &y& Elsevier]

Abstract: In problem of portfolio selection, financial Decision Makers (DMs) explain objectives and investment purposes in the frame of multi-objective mathematic problems which are more consistent with decision making realities. At present, various methods have introduced to optimize such problems. One of the optimization methods is the Compromise Programming (CP) method. Considering increasing importance of investment in financial portfolios, we propose a new method, called Nadir Compromising Programming (NCP) by expanding a CP-based method for optimization of multi-objective problems. In order to illustrate NCP performance and operational capability, we implement a case study by selecting a portfolio with 35 stock indices of Iran stock market. Results of comparing the CP method and proposed method under the same conditions indicate that NCP method results are more consistent with DM purposes. [Copyright &y& Elsevier]

In this paper, we attempt to introduce an algorithm which considers not only time factor but also cost, risk, and quality criteria to determine the critical path under fuzzy environment. In this algorithm first, decision makers allocate time, cost, risk, and quality to each activity. We are lacking in data and information, so in the proposed algorithm, the ratings of each activity and the weight of each criterion are described by fuzzy numbers and linguistic variables, which can be expressed in triangular fuzzy number. Linguistic variables are applied to represent the intensity of preferences of one criterion over another. Then, we add up triangular fuzzy numbers to determine the final evaluation value of each criterion for paths. Next, we use fuzzy TOPSIS, a technique for order preferences by similarity to an ideal solution, a method proposed by the authors in another paper, to choose the best alternative. Finally, numerical example is solved to illustrate the procedure of proposed method at the end of this paper. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a method for transient analysis of availability and survivability of a system with the identical components and identical repairmen. The considered system is supposed to consist of series of k-out-of-n or parallel components. We employed the Markov models, eigen vectors and eigenvalues for analyzing the transient availability and survivability of the system. The method is implemented through an algorithm which is tested in MATLAB programming environment. The new method enjoys a stronger mathematical foundation and more flexibility for analyzing the transient availability and survivability of the system. [Copyright &y& Elsevier]

This paper incorporates the preventive maintenance activities into the two-stage assembly flow-shop scheduling problem, which has m machines at the production stage and an assembly machine at the second one. In this paper, three maintenance policies are employed based on the maximization of machine's reliability and availability. This problem is NP-hard. So, two hybrid optimization methods, namely variable neighborhood search together with simulated annealing algorithm, and variable neighborhood search along with tabu search algorithm, are used to find the proper job sequencing. Statistical methods are conducted based on the hypothetical testing and analysis of variance to determine the better solution method and sensitivity analysis. Also, the performances of the presented algorithms on the problems of different sizes are analyzed. Results have shown that variable neighborhood search with simulated annealing outperforms another algorithm in the quality of solutions and computational time. [ABSTRACT FROM AUTHOR]

The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria' objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method's results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria. [ABSTRACT FROM AUTHOR]

The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial problems in managing supply chains is the process of evaluation and selection of green suppliers. This process can be categorized as a multi-criteria decision-making (MCDM) problem. The aim of this study is to propose a novel and efficient methodology for evaluation of green construction suppliers with uncertain information. The framework of the proposed methodology is based on weighted aggregated sum product assessment (WASPAS) and the simple multi-attribute rating technique (SMART), and Fermatean fuzzy sets (FFSs) are used to deal with uncertainty of information. The methodology was applied to a green supplier evaluation and selection in the construction industry. Fifteen suppliers were chosen to be evaluated with respect to seven criteria including "estimated cost", "delivery efficiency", "product flexibility", "reputation and management level", "eco-design", and "green image pollution". Sensitivity and comparative analyses were also conducted to assess the efficiency and validity of the proposed methodology. The analyses showed that the results of the proposed methodology were stable and also congruent with those of some existing methods. [ABSTRACT FROM AUTHOR]

• A bi-objective mathematical model is developed to optimize the reliability of a complicated system. • Different redundancy strategies with component mixing can be used in each subsystem. • An efficient simulation procedure is developed to estimate MTTF. • Multi Objective Water Flow algorithm (MOWFA) is proposed and compared to NSGA-II and NRGA. • Several multi-objective metrics are calculated to compare the performance of methods. Redundancy allocation is one of the most common approaches to increase the system reliability. In this study, a new model is developed to maximize mean time to failure and to minimize the cost of a system. In general, many researchers are now considering the active redundancy even more than before; however, it is possible for a particular system design to utilize active redundancy and warm-standby redundancy as well. In this model, each subsystem can use both active and warm-standby strategies simultaneously. Moreover, the model allows for component mixing such that components of different types may be used in each subsystem. Thus, the aim of the proposed model is to select the best redundancy strategy, components' types and levels of redundancy for each subsystem. The simulation and neural network methods are applied considering the structural complexity of the model and repairable components. In order to solve the problem, meta-heuristic of Multi Objective Water Flow algorithm (MOWFA) is proposed and compared to NSGA-II and NRGA. Also, for tuning the meta-heuristics parameters, the Taguchi design of experiments is employed. The algorithms are used to solve 32 test problems and the results are compared. Finally, the results are analyzed and discussed. [ABSTRACT FROM AUTHOR]

The polyethersulfone (PES)‐based composite polymers have been extensively studied for many years as the core of various scientific activities, such as fuel cell production, water treatment, reverse osmosis, ultrafiltration, nanofiltration, and gas separation, which represent a significant field of research for several applications. In the present study, PES and polyvinylpyrrolidone‐coated magnetic core–shell nanoparticles (NPs) are synthesized. Following this, a range of these NPs and PES blend polymers are prepared. Then, analyzing methods are used for identification and characterization of a variety of these samples. Since some properties of the samples have been improved, and others are attenuated, the best composition could not be selected from the samples without a logical process. To do so, a multiple‐criteria decision‐making (MCDM) technique that considers all the desired parameters is utilized. A MCDM algorithm is applied to create a ranking of alternative properties from the best to the worst, alongside mathematical calculations which accounts for the relative importance of the criteria. Results have shown that PES film with 15% of polymer‐coated NPs presents the best performance based on the desired properties. [ABSTRACT FROM AUTHOR]

Determination of subjective weights, which are based on the opinions and preferences of decision-makers, is one of the most important matters in the process of multi-criteria decision-making (MCDM). Step-wise Weight Assessment Ratio Analysis (SWARA) is an efficient method for obtaining the subjective weights of criteria in the MCDM problems. On the other hand, decision-makers may express their opinions with a degree of uncertainty. Using the symmetric interval type-2 fuzzy sets enables us to not only capture the uncertainty of information flexibly but also to perform computations simply. In this paper, we propose an extended SWARA method with symmetric interval type-2 fuzzy sets to determine the weights of criteria based on the opinions of a group of decision-makers. The weights determined by the proposed approach involve the uncertainty of decision-makers’ preferences and the symmetric form of the weights makes them more interpretable. To show the procedure of the proposed approach, it is used to determine the importance of intellectual capital dimensions and components in a company. The results show that the proposed approach is efficient in determining the subjective weights of criteria and capturing the uncertainty of information. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a multi-objective mathematical programming model for the trade-off of time, cost, and quality in the project-scheduling problem (PSP) by taking priorities and resource constraints as well as activity preemption into account. First, a small-sized problem instance that was a sub-project of an oil and gas construction project was used for te validation of the proposed model and algorithm. Subsequently, considering the sensitivity, complexity, and importance of oil and gas projects, the proposed model was implemented in a large-sized oil and gas construction project. Considering the NP-hardness of this problem, the NSGA-II metaheuristic algorithm was used to deal with the time, cost, and quality trade-off problem. Finally, a sensitivity analysis was implemented on the three main parameters of time, cost, and quality to investigate the effects of changes on the results. The findings show that the proposed model is more sensitive to cost changes, so an increase in project costs leads to a drastic change in the values of other objective functions. [ABSTRACT FROM AUTHOR]

The main goal of green supply chain management (GSCM) is to reduce the negative environmental impacts in all activities and stages of a supply chain. Evaluation of suppliers in a supply chain according to environmental criteria can help us to achieve this goal of GSCM. Since this evaluation usually comprises some alternatives and some criteria, green supplier selection (GSS) could be considered as a multi-criteria decision-making (MCDM) problem. To handle the uncertainty of information in an MCDM problem, the theory of fuzzy sets is an effective tool. Interval type-2 fuzzy sets (IT2FSs), which are characterized by an interval membership function, is very flexible to model the uncertainty of the MCDM problems. In this study, a new integrated approach based on Weighted Aggregated Sum Product Assessment (WASPAS) method, is proposed to deal with multi-criteria group decision-making problems with IT2FSs. This approach is based on the operators of IT2FSs, some modifications in the classical WASPAS method and a new procedure for calculation of criteria weights. In the procedure of calculation of criteria weights, we combine the subjective weights expressed by decision-makers with objective weights resulted from an entropy method to obtain more realistic weights. To show the applicability of the proposed approach in the real-world MCDM problems, a green supplier selection problem is used. We perform a sensitivity analysis with different weights of criteria and different values of method's parameters to show the stability of the proposed approach. This analysis shows that combining the subjective and objective weights can help to increase the stability of the proposed approach with different weights of criteria. A comparison is also made between the results of the proposed approach and some existing methods for validating the proposed approach. This analysis shows that the proposed approach is efficient and well consistent with the other methods. [ABSTRACT FROM AUTHOR]

• Identifying the usability criteria of the blockchain in the supply chain; • Developing the NGPBWM II and LGPBWM II models for individual decision-making; • Reducing the number of constraints of group BWM models compared to previous models; • Combining NGPBWM II and the proposed group preference aggregation approach. The use of blockchain technology leads to improved operations and supply chain (SC) integration. Moreover, identifying and evaluating the blockchain technology application criteria in the logistics system is a Multi-Criteria Decision-Making (MCDM) challenge that requires taking into account the perspectives of experts with varying degrees of SC expertise. The purpose of this paper is to propose a novel group decision-making method based on the best-worst method (BWM) to evaluate the criteria of implementing blockchain technology in the SC. The proposed approach provides a mechanism whereby opinions of decision-makers (DMs) are aggregated in nine steps, first. Then, weights of criteria are determined using BWM individual decision-making models. Moreover, two individual decision-making methods called nonlinear goal programming based BWM II (NGPBWM II) and linear goal programming based BWM II (LGPBWM II) are extended in this study, which can be adopted in both individual and group decision-making problems. The NGPBWM II and LGPBWM II methods can be used for both individual and group decision-making. This study proposes a novel group decision-making framework. The framework has fewer constraints than previous group BWM models and can consider different best (worst) criteria by different DMs. The effectiveness of the proposed methodology is investigated employing eight numerical examples. The results reveal the high accuracy of the NGPBWM II in all eight examples. Therefore, a combination of the proposed group decision-making method and NGPBWM II is applied in a real case to evaluate the application criteria of the blockchain technology in the automotive industry SC, which was a new application for using blockchain technology in SC. [ABSTRACT FROM AUTHOR]

Abstract: The “bullwhip” effect is a major cause of supply chain deficiencies. This phenomenon refers to grow the amplification of demand or inventory variability as it moves up the supply chain. Supply chain managers experience this variance amplification in both inventory levels and orders. Other side, dampening variance in orders may have a negative impact on customer service due to the increase in the inventory variance. This paper with simulating a three stage supply chains consisting of a single retailer, single wholesaler and single manufacturer under both centralized and decentralized chains. In this paper, it is intended to analysis the causes of bullwhip effect from two dimensions of order and inventory variance using the response surface methodology. The results show that in both supply chains, rationing factor is considered as the least important cause of bullwhip effect. While the wholesaler’s order batching and the chain’s order batching are considered as the main causes for the bullwhip effect in the decentralized and centralized chains, respectively. [Copyright &y& Elsevier]

In multi criteria Decision Making, the decision maker wants to find the best alternative among a set of alternatives in order to satisfy a set of criteria. Traditionally, decision making models are based on crisp data. The shortcoming of these data in capturing the reality and lack of information persuaded researchers to develop decision making methods with uncertain data. In this paper, the ELECTRE method is extended with black numbers, under ambiguous environment. The proposed method is applied in a supplier selection problem. It's an outstanding method that can be used in real world problems with ill-defined and incomplete data. [ABSTRACT FROM AUTHOR]

In the field of supply chain management and logistics, using vehicles to deliver products from depots to customers is one of the major operations. Before using vehicles, optimizing the location of depots is necessary in a location-routing problem (LRP). Also, before transportation products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. In this paper, the mathematical modelling of LRP is developed according to the existing condition and constraint in the real world. Maximum travelling time constraint is added, and we apply fuzzy numbers to determine customer demands, travelling time and drop time. The objective is to open a subset of depots to assign customers to these depots and to design vehicle routes, in order to minimize both the cost of open depots and the total cost of the routes. The proposed problem is modelled as a fuzzy linear programming (FLP), by applying the fuzzy ranking function method; the proposed FLP is converted to an exact linear programming (LP). A Lingo solver is used to solve this LP model in very small size. LRP is an non-deterministic polynomial-time hard (NP-Hard) problem, and because of the limitation of Lingo solver in solving medium, and large-size numerical examples, a hybrid algorithm including simulated annealing and mutation operator is proposed to solve these numerical examples. Also, a heuristic algorithm is proposed to find a suitable initial solution which is used in hybrid algorithm. At the end, a different analysis of the applied algorithm and a proposed model are introduced. [ABSTRACT FROM AUTHOR]

The swiftly growing wind farm projects together with the increasing uncertainty of these types of construction projects makes it more probable that projects will face delay. This research aims to introduce a heuristic algorithm to determine the sizes of project buffer and feeding buffers as well as dynamically control buffer consumption, named as Fuzzy Overlapping Buffer Management Algorithm (FOBMA). In this paper, the pentagonal fuzzy numbers were used to determine the appropriate amount of project activity resources. Also, an overlapping method was applied to obtain more realist activity durations. The FOBMA algorithm was compared to other algorithms using the data of 15 real-world wind farm projects as well as 15 randomly generated numerical examples. Moreover, the sensitivity analysis was conducted to validate the performance of the proposed algorithm. The findings demonstrate that the proposed FOBMA algorithm outperforms the other algorithms and yielded more realistic and shorter project duration. • A heuristic Fuzzy Overlapping Buffer Management Algorithm (FOBMA) is proposed. • The algorithm simultaneously calculates the sizes of project buffer and feeding buffers, and controls buffer consumption dynamically. • Pentagonal fuzzy numbers were used to determine the amount of project activity resources. • An overlapping method was applied to obtain more realist activity durations. • The performance of this algorithm was compared to the other algorithms using a range of actual projects and randomly generated numerical examples. • The findings demonstrate that the algorithm outperforms the other algorithms. [ABSTRACT FROM AUTHOR]