One of the most strategic and the most significant decisions in SUPPLY CHAIN management is reconfiguration of the structure and DESIGN of the SUPPLY CHAIN NETWORK. In this paper, a CLOSED LOOP SUPPLY CHAIN NETWORK DESIGN model is presented to select the best tactical and strategic decision levels simultaneously considering the appropriate transportation mode in activated links. The strategic decisions are made for a long term; thus, it is more satisfactory and more appropriate when the decision variables are considered uncertain and fuzzy, because it is more flexible and near to the real world. This paper is the first research which considers fuzzy decision variables in the SUPPLY CHAIN NETWORK DESIGN model. Moreover, in this study a new fuzzy optimization approach is proposed to solve a SUPPLY CHAIN NETWORK DESIGN problem with fuzzy tactical decision variables. Finally, the proposed approach and model are verified using several numerical examples. The comparison of the results with other existing approaches confirms efficiency of the proposed approach. Moreover the results confirms that by considering the vagueness of tactical decisions some properties of the SUPPLY CHAIN NETWORK will be improved.

Undoubtedly, metals are the basis of the sustainable development of all human societies. In the last century, the role of copper, as the third most widely used metal, after steel and aluminum, has been crucial. Copper is a recyclable metal. It has many applications such as industrial electricity, plumping, wiring, electronic equipment, transportation, and infrastructure. Today, with the growth of the industry in societies, the demand for copper has increased. This motivated us to study its SUPPLY CHAIN NETWORK DESIGN firstly. To the best of our knowledge, there is no research reported about copper SUPPLY CHAIN NETWORK DESIGN. This paper aims to maximize the profit of the copper CLOSED-LOOP SUPPLY CHAIN. We formulate this NETWORK DESIGN problem as a Mixed Integer Programming model. The model is considered as single-objective and multi-product. The exact solution of the model is found by using GAMS software. Sensitivity analysis results provide useful results that managers can use them in decisions.

Consideration of environmental and social issues in addition to economic ones is a critical strategy that companies pay special attention to DESIGNing their SUPPLY CHAIN. A resilient system prevents organizations from being surprised by catastrophic disruptions and critical conditions and eliminates high unwanted costs. In this study, a mixed-integer mathematical programming model is proposed to DESIGN a sustainable and resilient CLOSED-LOOP SUPPLY CHAIN NETWORK. Since suppliers are the most important external players, the slightest probability of disruptions can have a significant impact on CHAIN performance. Accordingly, applying efficient strategies can be very helpful for coping with them. Also, because of the uncertain nature of some input parameters, the P-robust optimization method has been used to tackle them. An efficient algorithm has been carried out beside a heuristic method based on the strategic variables relaxation to solve the model. A case study of a lighting projectors industry has been conducted to evaluate the efficiency of the proposed approach. Finally, sensitivity analysis is performed on critical parameters of the problem. By solving the example, it is seen that 3 primary suppliers and 3 backups are selected, and 3 production centers, 2 collection centers and 1 repair, recycling and disposal centers have been established. The value of the economic objective function is equal to 565. 857552 monetary units (MU). The CL-SCN environmental score is 658. 07, while it is 608. 93 in the social dimension. Eventually, the value of the final multi-objective function is equal to 0. 658.

Recent papers in the concept of SUPPLY CHAIN NETWORK DESIGN (SCND) have seen a rapid development in applying the stochastic models to get closer to real-world applications. Regaring the special characteristics of each product, the stracture of SCND varies. In tire industry, the recycling and remanufacturing of scraped tires lead to DESIGN a CLOSED-LOOP SUPPLY CHAIN. This paper proposes a two-stage stochastic model for a CLOSED-LOOP SCND in the application of tire industry. The first stage of model optimizes the expected total cost. Then, financial risk has been considered as the second stage of model to control the uncertainty variables leading to a robust solution. To solve the developed problem, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have been used. To enhace the efficiency of metaheuristic algorithms, Response Surface Method (RSM) has been applied. Finally, the proposed model is evaluated by different test problem with different complexity and a set of metrics in terms of Pareto optimal solutions.

In this research, a CLOSED-LOOP green SUPPLY CHAIN NETWORK is DESIGNed under uncertain conditions. In the proposed model, four objective functions including minimizing NETWORK costs, minimizing greenhouse gas emissions, minimizing production-technical risk, and minimizing the time of sending products to customers are considered simultaneously. Using the proposed NETWORK, it is possible to manage the flow of raw materials, first-hand products, and return products between facilities, production planning for each production center, how to allocate products to each facility, determine the number of manpower required for employment and training in each production center, how to allocate machinery and equipment, as well as time management by determining the minimum acceptable time to send products to customers so that the NETWORK has the least cost, the least amount of greenhouse gas emissions from the operational processes of facilities and transportation and has made strategic decisions with the least production-technical risk and the least time possible. In this research, to increase the efficiency of the model, parameters such as the amount of return product, recycling rate, and destruction are considered indefinitely and fuzzy logic is used to eliminate the uncertainty. Finally, due to the breadth of the model, the model is validated using a genetic algorithm. The result of the validation indicates the efficiency of the proposed model in optimizing the CLOSED-LOOP green SUPPLY CHAIN NETWORK.

displacement between levels, and maximize the reliability of delivery for suppliers. This NETWORK is including supplier centers, production/resuscitation centers, distribution/collection centers, customer centers, and disposal centers. A new Linear Integer programming model is formulated. Besides, fuzzy parameters and multi-objective function are used to approach the real world. In this regard, a deterministic two-step approach is used to consider the uncertainty in the proposed model. Finally, the performance and efficiency of the proposed model and solution methods are simulated in the numerical example and examined and suggestions are presented for using this model in the real world.

Nowadays, manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of SUPPLY CHAIN planning. On the other hand, increasing worldwide environmental, lack of food resources and social concerns are motivating manufacturers and consumers to implement recycling strategies such as product recovery, waste management, or usage of recycled materials. In this study, the CLOSED-LOOP SUPPLY CHAIN NETWORK has been proposed which consists of four echelons (suppliers, plants, distribution centers, and customers) in the forward CHAIN and three echelons (collection centers, inspection centers, and disposal centers) in the backward CHAIN. We present a multi-product and multi-period mixedinteger linear programming problem in this paper. The objective of this study is to maximize the profit in the CLOSED-LOOP SUPPLY CHAIN NETWORK. The proposed model is applied to an illustrative example based on inspiration from the dairy industry in Iran. The solution of the proposed model is achieved by using Gams software. The results give important insight for fostering the decision making process.

By increasing attention to environmental issues, the problem of DESIGN CLOSED-LOOP SUPPLY CHAIN has been more important. The integrated DESIGN of CLOSED-LOOP SUPPLY CHAINs as one of the most important issues in the management of SUPPLY CHAINs involve determining the location and number of required facilities (production, collection, recycling and disposal) in the forward and reverse SUPPLY CHAIN, inventories in every facility and flows between them. In this paper, a CLOSED-LOOP SUPPLY CHAIN with diverse products (multi-product) has been studied and a linear bi-objective mathematical model is proposed to reduce the total costs and the emissions in the NETWORK with determining the strategic and operational variables. Because of the uncertainty in parameters of proposed model such as customer demands or returns, the proposed model under uncertainty (robust optimization) is developed. The CLOSED-LOOP SUPPLY CHAIN of glass bottles is studied and modeled to minimize the total costs and production of carbon dioxide by proposed model. Finally, a sensitivity analysis of robust optimization model was conducted.

Objective: Attention to environmental issues in SUPPLY CHAIN activities has been taken into consideration due to the increase in public awareness and strict laws related to environmental protection. Initially, only the economic aspects of the SUPPLY CHAIN were considered in the NETWORK configuration, but with increasing concerns about environmental issues, reverse logistics and CLOSED-LOOP SUPPLY CHAINs were developed. DESIGNing a CLOSED-LOOP SUPPLY CHAIN NETWORK plays an important role in reducing costs, improving service levels, and responding to environmental issues. Therefore, the purpose of this study is to DESIGN a CLOSED-LOOP SUPPLY CHAIN NETWORK taking into account hybrid uncertainties and flexibility in constraints. Methods: In most of the conducted studies about SUPPLY CHAIN NETWORK DESIGN, the types of cognitive and random uncertainties, as well as the flexibility of soft constraints, have not been investigated simultaneously, while the conducted modeling is not able to consider hybrid uncertainty in SUPPLY CHAIN parameters in the real world. In this study, to simultaneously consider the hybrid uncertainties and flexibility in constraints, a novel model of robust stochastic, possibilistic, and flexible programming based on Me measurement was developed. In this model, the convex combination of optimistic and pessimistic attitudes of decision-makers was considered in the form of the Me measure, and the modeling was more flexible and realistic. Results: In the proposed approach, a convex combination of optimistic and pessimistic spectra was considered in the model. The need for subjective and repetitive reviews by decision-makers was eliminated in the model and the level of satisfaction was calculated optimally after solving the problem. On the other hand, due to the robustness of the model, possible deviations, scenario deviations, non-fulfillment of demand and capacity, and deviations of soft constraints were minimized. In the proposed approach based on the Me measure, the problem-solving approach was reduced and there was no need for a two-step solution to find solutions. Conclusion: A case study was conducted in the SUPPLY CHAIN of stone paper production to evaluate the efficiency of the proposed model. The results of sensitivity analysis, robustness analysis, and simulation with the realization model showed that the proposed model was able to provide robust and realistic solutions. The proposal of a realistic and flexible solution for DESIGNing problems of the SUPPLY CHAIN NETWORK by creating a trade-off between the objective function and the risk-taking level of decision-makers and managers through changing the justified space in the Me criterion in the proposed approach was one of the achievements of the present study. As its other achievement, the present study could provide a combination of different viewpoints of decision-makers’ risk-taking through changing the justified space based on different values of the parameter λ in measuring Me and propose flexible and realistic solutions according to the results of numerical simulation in the proposed approach.

The interaction of sustainability and resilience has not been sufficiently addressed in the SUPPLY CHAIN literature. Applying sustainability and resilience concepts in a SUPPLY CHAIN means the simultaneous optimization of the cost and recourses, including human and environmental ones, in facing possible risks. This paper tries to fill this gap by considering them simultaneously in a CLOSED-LOOP SUPPLY CHAIN. For this, a new mixed-integer programming (MIP) model was formulated for a CLOSED-LOOP SUPPLY CHAIN. In this research, different dimensions of sustainable development have been taken into account through reducing the total cost, energy consumption, and pollution and increasing job opportunities, and multiplesourcing strategy has been applied for the resiliency of the SUPPLY CHAIN. To validate the proposed model, the real data of a tire industry was used and the model was solved using the -Constraint method. The results emphasized the necessity of combining sustainability and resilience in a CLOSED-LOOP SUPPLY CHAIN, where the high amounts of demand, in addition to increasing the cost, energy consumption, and pollution, increase job opportunities and the need to backup suppliers for raw materials.