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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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Abstract

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.

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Authors and Affiliations

  1. Department of Industrial Engineering, Babol Noshiravani University of Technology, Babol, 47148-71167, Iran

    Niloofar Nadim Kabiri, Saeed Emami & Abdul Sattar Safaei

Authors
  1. Niloofar Nadim Kabiri
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  2. Saeed Emami
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  3. Abdul Sattar Safaei
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Correspondence to Saeed Emami.

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Appendix

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See Tables

Table 14 Definition of the sets
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Table 15 Parameters of the FLM
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Table 16 Parameters of the SLM
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Table 17 Parameters of the TLM
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Table 18 Variables of the FLM
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Table 19 Variables of the SLM
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Table 20 Variables of the TLM
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Kabiri, N.N., Emami, S. & Safaei, A.S. Simulation–optimization approach for the multi-objective production and distribution planning problem in the supply chain: using NSGA-II and Monte Carlo simulation. Soft Comput 26, 8661–8687 (2022). https://doi.org/10.1007/s00500-022-07152-2

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