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Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation

Author

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  • Xiaoyu Yu

    (School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China)

  • Jingyi Qian

    (School of Electronic and Information Engineering, Tongji University, Shanghai 200092, China)

  • Yajing Zhang

    (School of Economics and Management, Anhui Normal University, Wuhu 241000, China)

  • Min Kong

    (School of Economics and Management, Anhui Normal University, Wuhu 241000, China)

Abstract

The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port delivery stage. To solve this problem, we first analyze several sufficient and necessary conditions of the optimal scheme. We then propose a heuristic algorithm based on a dynamic programming algorithm to obtain the optimal solution for a special case where the assignment of all ingots to the soaking pits is known. Based on the results of this special case, we develop a modified biased random-key genetic algorithm (BRKGA), which incorporates genetic operations based on the flower pollination algorithm (FPA) to obtain joint production and distribution schedules for the general problem. Finally, we conduct a series of computational experiments, the results of which indicate that BRKGA-FPA has certain advantages in solving quality and convergence speed issues.

Suggested Citation

  • Xiaoyu Yu & Jingyi Qian & Yajing Zhang & Min Kong, 2023. "Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation," Mathematics, MDPI, vol. 11(15), pages 1-24, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3276-:d:1202511
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    References listed on IDEAS

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    1. Londe, Mariana A. & Pessoa, Luciana S. & Andrade, Carlos E. & Resende, Mauricio G.C., 2025. "Biased random-key genetic algorithms: A review," European Journal of Operational Research, Elsevier, vol. 321(1), pages 1-22.
    2. Carlos E. Lopez-Landeros & Ricardo Valenzuela-Gonzalez & Elias Olivares-Benitez, 2024. "Dynamic Optimization of a Supply Chain Operation Model with Multiple Products," Mathematics, MDPI, vol. 12(15), pages 1-23, August.

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