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A multi-population genetic algorithm for transportation scheduling

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

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    Article provided by Elsevier in its journal Transportation Research Part E: Logistics and Transportation Review.

    Volume (Year): 45 (2009)
    Issue (Month): 6 (November)
    Pages: 946-959

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    Handle: RePEc:eee:transe:v:45:y:2009:i:6:p:946-959
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