Designing a sustainable integrated production system model under uncertainty considering the discount in production outsourcing costs
الموضوعات :Saeed Shahdoust 1 , Mohammad Fallah 2 , Esmaeil Najafi 3
1 - 1Department of Industrial Engineering, Islamic Azad University, Central Tehran Branch
2 - Department of Industrial Engineering, Islamic Azad University, Central Tehran Branch
3 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
الکلمات المفتاحية: sustainable integrated production system, robust-fuzzy-probabilistic optimization method, MOGWO algorithm,
ملخص المقالة :
Sustainability in the integrated production system in supply chain networks has led to the creation of competitive advantage for companies. Therefore, companies should have proper management of their supply chain network to increase their market share. Therefore, in this article, a dual-objective model of a sustainable integrated production system is presented, taking into account the simultaneous reduction of possible costs on the system, the amount of greenhouse gas emissions, and the application of discounts on the costs of outsourcing production under uncertainty and the control of non-deterministic parameters with the robust optimization method. . Due to the NP-hard nature of the problem, the exact epsilon constraint method and meta-heuristic algorithms MOPSO, NSGA II, and MOGWO have been used to solve the model. The results of the calculations showed that the NSGA II algorithm is effective in obtaining the indicators of the number of effective answers and the distance index, and the MOPSO algorithm is also in obtaining the indicators of the most spread, the average computing time, and the distance from the ideal point, and the MOGWO algorithm is also in obtaining the averages of the first objective function . Also, the results of the implementation of the TOPSIS method for ranking the algorithms for solving the problem of a sustainable integrated production system included obtaining a desirability weight of 0.5882 for the MOPSO algorithm, obtaining a desirability weight of 0.1397 for the MOGWO algorithm, and obtaining a desirability weight of 0.7491 for the NSGA II algorithm.
