عنوان مقاله [English]
In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing management and is regarded as one of the infrastructure and important concepts for implementing the career so that in many of them it is essentially tried to shorten the time between the customer’s order and the real time of delivering the goods. Cross docking is one of the most important alternatives for lowering the time in supply chain. The central aim of this paper is to focus on optimizing the planning of the trucks input and output aiming to minimize total time of operation inside the supply chain in designed model. Timing the transportation in this paper makes the time between sources and destinations, time of unloading and transferring the products minimized. To find the optimum answers to the question, genetic algorithms and the particle swarm optimization have been used. Then, these algorithms have been compared with the standards such as the implementation time and quality of answers with each other and then better algorithms in each standard identified.
Bartholdi, J. J. & Gue, K. R. (2004). The best shape for a crossdock. Transportation Science. 38 (2), 235–244.
Boloori Arabani, A.R. & Fatemi Ghomi, S.M.T. & Zandieh, M. (2011). Meta-heuristics implementation for scheduling of trailers in a cross-docking system with temporary storage. Expert Systems with Applications. 38 (3) 1964–1979.
Boysen, N. (2010). Truck scheduling at zero-inventory cross docking terminals. Computers & Operations Research. 37, 32 – 41.
Boysen, N. & Fliedner, M. (2010). Cross dock scheduling: Classification, literature review and research agenda. Omega. 38 (6), 413–422.
Chen, F. & Song, K.L. (2009). Minimizing makespan in two-stage hybrid cross-docking scheduling problem. Computers and Operations Research. 36 (6), 2066–2073.
Eberhart, R. & Kennedy, J. (1995). A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan.. pp 39.43.
Engelbrecht, A. P. (2005). Fundamentals of Computational Swarm Intelligence. West Sussex, England: John wiley & Sons, Ltd.
Hu, X. & Shi,Y. & Eberhart, R. (2004). Recent Advances in Particle Swarm. Paper presented at the Congress on Evolutionary Computation. CEC.
Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. In: Proceedings of the 1995IEEE international conference on neural networks. New Jersey: IEEE Press. pp 1942.1948.
Liao, T. W. & Egbelu, P.J. & Chang, P.C. (2013). Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations. Int. J. Production Economics. 141, 212–229.
Peck, K. E. (1983). Operational analysis of freight terminals handling less than container load shipments, PhD thesis, University of Illinois at Urbana-Champaign,Urbana. IL 61801.
Schaffer, B. (1998). Cross docking can increase efficiency. Automatic I.D. News. 14(8),34–37.
Tang, S.L. & Yan. H. (2010). Pre-distribution vs. post-distribution for cross-docking with Transshipments. Omega. 38 (3–4) 192–202.
Tsui, L. Y. & Chang, C. H. (1990). A microcomputer based decision support tool for assigning dock doors in freight yards. Computers & Industrial Engineering. 19(1–4), 309–312.
Yu, W. & Egbelu, P. J. (2008). Scheduling of inbound and outbound trucks in cross docking systems with temporary storage. European Journal of Operational Research. 184, 377–396.
Vahdani, B & Zandieh, M. (2010). Scheduling trucks in cross-docking systems: Robust meta-heuristics. Computers & Industrial Engineering. 58, 12–24.
Vis, F. A. & Roodbergen, K.J. (2011). Layout and control policies for cross docking operations. Computers & Industrial Engineering. 61, 911–919.
Witt, C.E. (1998). Crossdocing: Concepts Demand Choice. Material Handeling Engineering. 53(7).