Providing a Multi-Objective Mathematical Model for Closed-loop Supply Chain Optimization Manufacturing under uncertainty conditions

Supply chain optimization involves the integration of supply chain activities and related information flows by improving the state of chain relationships to achieve a secure and sustainable competitive advantage. In this realization, the issue of supply chain optimization has been chosen because to provide goods and services in the economy, we need a mechanism and system to maximize economic efficiency and productivity and provide return goods and services at lower and easier costs and the circular economy to be realized, the main discussion of which is related to the recycling of returned goods and goods that need to be repaired so that they can be reused. This research also emphasizes the use of healthy parts and components in the reproduction of products and this issue is considered for all goods in each part of the supply chain that cannot be used and must be transferred to the reproduction. The results of the implementation of the model presented in the genetic algorithm show that this algorithm was able to reach an almost optimal answer at the right time. This algorithm requires more computational time than LINGO optimization software to solve problems that have very small dimensions but this algorithm needs much less time to solve problems due to the increase in problem dimensions than LINGO optimization software. Therefor in these examples we observed that this algorithm can reach an acceptable answer in a much shorter interval than LINGO optimization software for large scale problems.