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An empirical study on solving an integrated production and distribution problem with a hybrid strategy

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Listed:
  • Feng Li
  • Li Zhou
  • Guangshu Xu
  • Hui Lu
  • Kai Wang
  • Sang-Bing Tsai

Abstract

Coordination is essential for improving supply chain performance, and one of the most critical factors in achieving the coordination of a supply chain is the integrated research of production and distribution. In this paper, a novel two-stage hybrid solution methodology is proposed. In the first stage, products are processed on the serial machines of multiple manufacturers located in two industrial parks. A fuzzy multi-objective scheduling optimization is performed using a modified non-dominated sorting genetic algorithm II (NSGA-II). The result obtained in the first stage is used in the second stage to optimize the distribution scheduling problem using a modified genetic annealing algorithm (GAA). Finally, simulation results verify both the feasibility and efficiency of the proposed solution methodology.

Suggested Citation

  • Feng Li & Li Zhou & Guangshu Xu & Hui Lu & Kai Wang & Sang-Bing Tsai, 2018. "An empirical study on solving an integrated production and distribution problem with a hybrid strategy," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-23, November.
    • Handle: RePEc:plo:pone00:0206806
    DOI: 10.1371/journal.pone.0206806
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    References listed on IDEAS

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    Cited by:

    1. Wenzhu Liao & Tong Wang, 2019. "A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission," Sustainability, MDPI, vol. 11(10), pages 1-27, May.

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