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چکیده
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Designing a sustainable and resilient supply chain network is an important area in academic research that has the potential to influence supply chain performance. Increasing regulations in various aspects of sustainability, including environmental protection, is forcing companies to consider their supply chains for economic, environmental and social purposes. On the other hand, the performance of the supply chain may be disrupted for a variety of natural or human reasons. Therefore, designing an efficient supply chain network that is flexible enough in case of disturbances, is a new challenge for supply chain managers. This paper focuses on supply chain sustainability and resilience in which various scenarios such as product life cycle and environmental compatibility, carbon emissions, effluents, and wastes as well as scenarios for managing the occurrence of failure among members are considered. In order to solve the model, Meta-heuristic algorithms, Non-dominated Sorting Genetic Algorithm (NSGA-II) and subpopulation genetic algorithm (SPGA-II) have been used to find optimal solutions. To compare the efficiency of two algorithms several sample problems are generated and solved that show the Sub-population Genetic Algorithm (SPGA- II) can find good solutions in a reasonable time limit
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