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Population-Based Meta-Heuristic Algorithms for Integrated Batch Manufacturing and Delivery Scheduling Problem

Author

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  • Yong-Jae Kim

    (Department of Industrial and Management Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

  • Byung-Soo Kim

    (Department of Industrial and Management Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

Abstract

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time.

Suggested Citation

  • Yong-Jae Kim & Byung-Soo Kim, 2022. "Population-Based Meta-Heuristic Algorithms for Integrated Batch Manufacturing and Delivery Scheduling Problem," Mathematics, MDPI, vol. 10(21), pages 1-22, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4127-:d:963929
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    References listed on IDEAS

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    1. Gregory Dobson & Ramakrishnan S. Nambimadom, 2001. "The Batch Loading and Scheduling Problem," Operations Research, INFORMS, vol. 49(1), pages 52-65, February.
    2. Gao, Su & Qi, Lian & Lei, Lei, 2015. "Integrated batch production and distribution scheduling with limited vehicle capacity," International Journal of Production Economics, Elsevier, vol. 160(C), pages 13-25.
    3. Fateme Marandi & S.M.T. Fatemi Ghomi, 2019. "Integrated multi-factory production and distribution scheduling applying vehicle routing approach," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 722-748, February.
    4. Selvarajah, Esaignani & Steiner, George, 2006. "Batch scheduling in a two-level supply chain--a focus on the supplier," European Journal of Operational Research, Elsevier, vol. 173(1), pages 226-240, August.
    5. Zhang, Jun & Wang, Xuping & Huang, Kai, 2018. "On-line scheduling of order picking and delivery with multiple zones and limited vehicle capacity," Omega, Elsevier, vol. 79(C), pages 104-115.
    6. Xinbao Liu & Shaojun Lu & Jun Pei & Panos M. Pardalos, 2018. "A hybrid VNS-HS algorithm for a supply chain scheduling problem with deteriorating jobs," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5758-5775, September.
    7. Low, Chinyao & Chang, Chien-Min & Li, Rong-Kwei & Huang, Chia-Ling, 2014. "Coordination of production scheduling and delivery problems with heterogeneous fleet," International Journal of Production Economics, Elsevier, vol. 153(C), pages 139-148.
    8. Lee, C. -Y. & Leon, V. J., 2001. "Machine scheduling with a rate-modifying activity," European Journal of Operational Research, Elsevier, vol. 128(1), pages 119-128, January.
    9. Cheng, Ba-Yi & Leung, Joseph Y-T. & Li, Kai, 2017. "Integrated scheduling on a batch machine to minimize production, inventory and distribution costs," European Journal of Operational Research, Elsevier, vol. 258(1), pages 104-112.
    10. Li, Kai & Jia, Zhao-hong & Leung, Joseph Y.-T., 2015. "Integrated production and delivery on parallel batching machines," European Journal of Operational Research, Elsevier, vol. 247(3), pages 755-763.
    11. B.‐Y. Cheng & J.Y.‐T. Leung & K. Li & S.‐L. Yang, 2015. "Single batch machine scheduling with deliveries," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(6), pages 470-482, September.
    12. İsmail Karaoğlan & Saadettin Erhan Kesen, 2017. "The coordinated production and transportation scheduling problem with a time-sensitive product: a branch-and-cut algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 536-557, January.
    13. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
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