Genetic algorithms for door-assigning and sequencing of trucks at distribution centers for the improvement of operational performance |
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| Authors: | Kangbae Lee Byung Soo Kim Cheol Min Joo |
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| Affiliation: | 1. Department of Management Information Systems, Dong-A University, Busan 602-760, Republic of Korea;2. Graduate School of Management of Technology, Pukyong National University, Busan 608-737, Republic of Korea;3. Department of Industrial and Management Engineering, Dongseo University, Busan 617-716, Republic of Korea;1. Industrial Management, School of Engineering, University of Seville, Ave. Descubrimientos s/n, E41092 Seville, Spain;2. Compañía Industrial de Aplicaciones Térmicas SA, Córdoba, Spain;1. School of Software, Dalian University of Technology, 116620, PR China;2. Institute of System Engineering, Dalian University of Technology, 116624, PR China;1. Research Group of Technology Applied to Optimization (GTAO), Universidade Federal do Paraná (UFPR), Curitiba, Brazil;2. Departamento de Administração Geral e Aplicada, Universidade Federal do Paraná, Botanico, Curitiba, Brazil;3. Centre Interuniversitaire de Recherche sur les Réseaux d''Entreprise, la Logistique et le Transport (CIRRELT), Canada;4. Département opérations et systèmes de décision, Université Laval, Québec, Canada;1. Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., 15916-34311 Tehran, Iran;2. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH44106-7070, USA;3. Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran |
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| Abstract: | In a supply chain, cross docking is one of the most innovative systems for improving the operational performance at distribution centers. By utilizing this cross docking system, products are delivered to the distribution center via inbound trucks and immediately sorted out. Then, products are shipped to customers via outbound trucks and thus, no inventory remains at the distribution center. In this paper, we consider the scheduling problem of inbound and outbound trucks at distribution centers. The aim is to maximize the number of products that are able to ship within a given working horizon at these centers. In this paper, a mathematical model for an optimal solution is derived and intelligent genetic algorithms are proposed. The performances of the genetic algorithms are evaluated using several randomly generated examples. |
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