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A multi-population genetic algorithm for transportation scheduling


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


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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    Cited by:

    1. 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.
    2. 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.


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