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A multi-population genetic algorithm for transportation scheduling

Author

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  • Zegordi, S.H.
  • Beheshti Nia, M.A.

Abstract

This study considers the integration of production and transportation scheduling in a two-stage supply chain environment. The objective function minimizes the total tardiness and total deviations of assigned work loads of suppliers from their quotas. After modeling the problem as a mixed integer programming problem, a genetic algorithm with three populations, namely, a multi-society genetic algorithm (MSGA), is proposed for solving it. MSGA is compared with the optimum solutions for small problems and a heuristic and a random search approach for larger problems. Additionally, an MSGA is compared with a generic genetic algorithm. The experimental results show the superiority of the MSGA.

Suggested Citation

  • Zegordi, S.H. & Beheshti Nia, M.A., 2009. "A multi-population genetic algorithm for transportation scheduling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 946-959, November.
    • Handle: RePEc:eee:transe:v:45:y:2009:i:6:p:946-959
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    Citations

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    Cited by:

    1. Lihong Pan & Miyuan Shan & Linfeng Li, 2023. "Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    2. Chen, Gang & Govindan, Kannan & Yang, Zhongzhen, 2013. "Managing truck arrivals with time windows to alleviate gate congestion at container terminals," International Journal of Production Economics, Elsevier, vol. 141(1), pages 179-188.
    3. Hao Zhang & Yan Cui & Hepu Deng & Shuxian Cui & Huijia Mu, 2021. "An Improved Genetic Algorithm for the Optimal Distribution of Fresh Products under Uncertain Demand," Mathematics, MDPI, vol. 9(18), pages 1-18, September.
    4. Ai, Yun-fei & Lu, Jing & Zhang, Li-li, 2015. "The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 170-188.
    5. Yu Zhou & Leishan Zhou & Yun Wang & Zhuo Yang & Jiawei Wu, 2017. "Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem," Complexity, Hindawi, vol. 2017, pages 1-14, July.

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