Supply chain network design includes key decisions that have a major impact on the supply chain operational structure. Efficient supply chain design improves performance in organizations. This has led to the emergence of new concepts in the supply chain issue in the pas More
Supply chain network design includes key decisions that have a major impact on the supply chain operational structure. Efficient supply chain design improves performance in organizations. This has led to the emergence of new concepts in the supply chain issue in the past decade. In this study, the supply chain network design problem in agile organizations has been taken into account with multi-level and multi-period. This problem is considered under conditions of having multiple customers with a high demand volume. The decisions include the selection of companies at each level, the amount of production, storage and transportation of each company. The problem has been modeled to integrate all decision variables with the goal of minimizing overall operating costs across the entire supply chain and Satisfaction of customers' complete demand and Satisfaction with them. Since multi-period multi-level supply chain design problem solving is one of the NP-Hard issues in uncertainty conditions, it is better to use innovative and meta-algorithms to reduce problem solving time. For this reason, the algorithm for banning search algorithms, which is one of the meta-algorithms, has been used to solve the model. The results of this research show that as the number of problem-solving repetitions increases, answers with less than 3% of the difference between the optimal answer are achieved. The search algorithm is forbidden to get the optimal response compared to the Lagrange algorithm.
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