A multi-population genetic algorithm for transportation scheduling
AbstractThis 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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Bibliographic InfoArticle provided by Elsevier in its journal Transportation Research Part E: Logistics and Transportation Review.
Volume (Year): 45 (2009)
Issue (Month): 6 (November)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description
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- 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.
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