فهرست نشریات علمی دسترسی آزاد ایران

Objective: While the closed-loop supply chain network was created launch, to design, and exploit the flow of materials between chain centers, supply chains face different risks, including operational ones and disruption. Each of such risks can lead to irreparable damage. Therefore, designing or redesigning supply chains to make them resilient against different risks is one of the most important programs that potentially affect the supply chain’s performance. The purpose of this research  is to design a resilient closed-loop supply chain network under the operational risks and disruption conditions by the Mulvey approach in Tehran's food industry companies with multi-products. Methods: In this research, the problem of the resilient closed-loop supply chain is considered as a network of nodes (production sites) that are connected by arcs (paths). The model is formulated as an integer programming, the objective function of which involves maximizing the number of nodes in estimating demand and minimizing costs based on a series of scenarios developed by the Mulvey approach. Results: In the practical phase, first, a closed-loop supply chain consisting of 10 manufacturers, 300 distributors, and two types of products was considered for modeling. Then, three more important scenarios with probabilities of 0.3, 0.2, and 0.5 were developed to present different amounts of customer demand and facilities capacity depending on the number of disruptions. Solving the problem for the multi-product food companies, using the LP metric model showed that despite no disruption in the supply chain, the robust optimal solutions for the first and second objective functions were equal to 99.484 and 790.50, respectively. In terms of manufactured products; in the first node, the amounts of products 1 and 2 did not change for the first and second scenarios but changed by 1.4 and 2.5 units in the third scenario. In the third node, the amount of product 1 did not change for the first scenario, but for the second and third scenarios, it changed by 10.60 and 6.8, respectively. The amount of product 2 did not change for the first and third scenarios but changed by 7.7 for the second scenario. In the 10th node, the amounts of products 1 and 2 did not change for the second scenario, but the amount of product 1 changed by 2.8 and 2.3 for the first and third scenarios. In addition, the amount of product 2 changed by 10.3 and 2.8 for the first and third scenarios, respectively. In the other nodes, the amounts of the products did not change. For both of the products in different nodes, except in nodes 4, 6, 7, and 9, and product 1 in node 8, some problems had to be fixed. Conclusion: According to the findings, developing the proposed model reduces operational risks and disruption as the most important causes of inefficiency in the supply chain. So, it is necessary to design robust and resilient supply chains in all industries, especially in the food industry due to its significance.