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Simulation-optimization approach for the multi-objective production and distribution planning problem in the supply chain: using NSGA-II and Monte Carlo simulation

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SOFT COMPUTING 2022年第17期26卷 8661-8687页

作者： Kabiri, Niloofar Nadim Emami, Saeed Safaei, Abdul Sattar Babol Noshiravani Univ Technol Dept Ind Engn Babol *** Iran

With the growth of multinational companies, increasing international and domestic competition between companies, upgrading information technology, and increasing customer expectations, accurate supply chain (SC) planning is essential. In such an environment, pollution has become more severe in recent decades, and with the weakening of the environment and global warming, green SC management strategies have become significant issues in recent decades. In this research, we consider the integrated production and distribution planning problem of a multi-level green closed-loop SC system, which includes multiple recycling, manufacturing/ remanufacturing, and distribution centers. We present a three-level bi-objective programming model to maximize profit and minimize the amount of greenhouse gas emissions. A hierarchical iterative approach utilizing the LP-metric method and the non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the proposed model. The Taguchi approach is applied to find optimum control parameters of NSGA-II. Moreover, Monte Carlo (MC) simulation is applied to tackle uncertainty in demand, and the NSGA-II algorithm is integrated with MC simulation (MCNSGA-II). Through numerical experiments on randomly generated instances, we observe that the average of the relative gaps NSGA-II and LP-metric method are 7%, 7%, and 1% for the three levels, respectively. Furthermore, it is observed that the value of convergence (C) and spacing (S) metrics of the MCNSGA-II algorithm are 0 and 1.82E + 07, which are better than NSGA-II.

关键词： Production and distribution planning Simulation-optimization Green closed-loop supply chain Monte Carlo simulation three-level bi-objective programming NSGA-II algorithm

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