A multi-population genetic algorithm for transportation scheduling

S.H. Zegordi and M.A. Beheshti Nia

Transportation Research Part E: Logistics and Transportation Review, 2009, vol. 45, issue 6, 946-959

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.

Keywords: Scheduling; Supply; chain; management; Genetic; algorithm; Transportation; Tardiness (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (5)

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