With increasing environmental pollution in recent years, researchers have focused on designing a closed loop supply chain network with consideration of environmental issues. In this research an uncertain bi-objective, multi-period, multi-product and multi-level closed-loop supply chain network is presented. Uncertainty in demand, transportation costs are considered and to counteract this uncertainty the robust optimization approach is used. The proposed supply chain network consists of four levels of forward supply chain and four levels of reverse chain. The proposed model is a mixed integer linear programming (MILP) model with the aim maximizing profit and minimizing generated pollution by transportation of products, and operational centers. The proposed model is solved by lingo software, so that the multi-objective model has been handled by utility based goal programming method. Finally, the results are analyzed and the comparison of different scenarios indicates that the objective function has strongly shown the uncertainty parameters and the effect of uncertainty in the parameters simultaneously. Therefore, network modeling based on different scenarios can be a good tool for deciding on confrontation with uncertain and ambiguous parameters.

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.

In today's complex world and in order to increase competitiveness, planners in the manufacturing systems have focused on product distribution and collection of used products. In this paper, the closed-loop supply chain scheduling problem is investigated for the first time. A comprehensive and integrated model is presented for production scheduling, delivering products to retailers using limited-capacity vehicles, and pick-upping end of life products in order to recycle and reuse in supply chain. The aim of this problem is to minimize maximum tardiness. Due to the fact that this problem is NP-hard, a genetic algorithm is presented to solve the large-size instances by obtaining near-optimal solutions. To illustrate the importance of the problem under consideration, a case study of the motor oil supply chain is presented.

One of the main components of competition in the current competitive environment is supply chain; therefore, organizations need to have a reliable supply chain to increase efficiency and effectiveness. Moreover, due to the increase in environmental pollution and the requirements imposed by the governments to harness polluting activities, organizations are obliged to follow green supply chain practices that account for environmental considerations along with economic aspects. hence, in this study, a bi-objective model for a green, closed-loop supply chain under demand uncertainty is proposed which takes into account environmental consideration and economic aspects. Another important aspect of the supply chain network design is the concept of uncertainty. Due to societal and political evolutions and the scarcity of raw materials in the decision-making horizon, uncertainty is a significant measure in the models of supply chain. Indeed, in this study, the model was developed for a supply chain under uncertainty so that more compatibility with real-world conditions would be achieved. The results show that considering uncertainties makes the model more flexible. The advancement of technology and unpredictable behaviors of customers in markets have created a very complex competitive atmosphere. To evaluate the performance of the developed model, the case study of the Iran Transfo Company is considered.

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.

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.

Due to factors such as political issues, technological change and natural disasters as well as the spread of environmental concerns and social responsibility, supply chain issues are no longer just a matter of looking ahead but the reverse-processing is also a matter of consideration. For this purpose, the present paper deals with the design and modeling of multi-periodic and multi-product green closed-loop supply chain in olive production. After designing the supply chain, the optimization of the whole chain profit and costs of the pollutants from the olive-product-processing process was investigated using a Genetic Algorithm based on Iranian olive data during the years 2016 to 2018. The model also came closer to reality by incorporating risk into the proposed model and taking into account the demand of all centers under risk conditions. The results show that the chain is profitable at 55417 billion Riyals, but with respect to the waste disposal to waste recycling cost-benefit ratio, 431. 91, Iran's olive supply chain performs poorly in waste recycling, which by incorporating this model and revising the chain, this ratio will be reduced by 79%. Furthermore, according to the results, there is no need for import in risky conditions and all the market’ s demand and export centers demand is met without any lost sales by the chain itself. It is, therefore, suggested to increase productivity in the field of olive-conversion-industries by equipping olive processing plants and constructing oil-production waste processing plants. On the other hand, in spite of the potentials and capacity of olive production and processing, it is suggested that macro-level policies be adopted to increase the consumption of olive oil in order to promote community health and create added value in order to increase the production of these products compared to its present value. . .

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.

Closed-loop supply chain network design is a critical issue due to its impact on both economic and environmental performances of the supply chain. In this paper, we address the problem of designing a multi-echelon, multi-product and capacitated closed-loop supply chain network. First, the problem is modeled with a mixed-integer, non-linear programming model that aims to maximize the total closed-loop supply chain profit. To reduce the complexity of the model, it was first linearized and solved by LINGO. Computational results and sensitivity analysis are conducted to demonstrate the performance of the proposed model. The main contribution of the proposed model is to address two economic viability issues of a closed-loop supply chain. The first issue is the collection of a sufficient quantity of end-of-life products that are assured by retailers against an acquisition price. The second issue lies in exploiting the benefits of the collocation of forward facilities and reverse facilities.

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.