The cross-docking system is a new distribution strategy which can reduce inventories, lead times, and improve responding time to customers. This paper considers biobjective problem of truck scheduling in cross-docking systems with temporary storage. The objectives are minimizing both makespan and total tardiness. For this problem, it proposes a multiobjective iterated greedy algorithm employing advance features such as modified crowding selection, restart phase, and local search. To evaluate the proposed algorithm for performance, it is compared with two available algorithms, subpopulation particle swarm optimization-II and strength Pareto evolutionary algorithm-II. The comparison shows that the proposed multiobjective iterated greedy algorithm shows high performance and outperforms the other two algorithms. 1. Introduction Efficiently managing the flow of products is one of the most essentialsteps in supply chain management. How this flow is handled is basically affected by transportation networks and distribution structures. Hence, any action contributing to the improvement of these structures such as the execution of cross-docking systems is considered worthwhile. In cross-docking system, products are received by inbound trucks in the receiving dock; then, they are unloaded, sorted, and reorganized based on customer demands. Afterwards, these products are loaded into the outbound trucks for delivery to customers, without being actually held as inventory at warehouse. If any item is held in storage, it takes usually a short time, generally less than 24？h. In comparison with traditional warehousing strategy, cross-docking systems can cut down or remove storing and retrieving functions, the two most expensive warehousing operations, by synchronizing the flows of inbound and outbound trucks. As a result, not only total operational costs are decreased as a result of reduction of a considerable level of inventory in the distribution system, but also the customers can be served by more precise and on-time shipment deliveries. The cross-docking system is the best way to handle high volume of items in a short time, reduce cost and space required for inventory (or eliminate storage), increase throughput, and improve efficiency by increasing level of customer satisfaction [1]. Thus, cross-docking becomes an attractive alternative to warehousing. The problem of truck scheduling in the cross-docking systems is to determine the sequence of inbound/outbound trucks to unload/load their products. Besides, the assignment of product transshipment is determined as well.
References
[1]
W. Yu and P. J. Egbelu, “Scheduling of inbound and outbound trucks in cross docking systems with temporary storage,” European Journal of Operational Research, vol. 184, no. 1, pp. 377–396, 2008.
[2]
A. Boloori Arabani, M. Zandieh, and S. M. T. Fatemi Ghomi, “A cross-docking scheduling problem with sub-population multi-objective algorithms,” International Journal of Advanced Manufacturing Technology, vol. 58, no. 5–8, pp. 741–761, 2012.
[3]
A. Boloori Arabani, M. Zandieh, and S. M. T. Fatemi Ghomi, “Multi-objective genetic-based algorithms for a cross-docking scheduling problem,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 4954–4970, 2011.
[4]
W. Yu, Operational strategies for cross docking systems [Ph.D. dissertation], Iowa State University, 2002.
[5]
A. R. Boloori Arabani, S. M. T. Fatemi Ghomi, and M. Zandieh, “Meta-heuristics implementation for scheduling of trucks in a cross-docking system with temporary storage,” Expert Systems with Applications, vol. 38, no. 3, pp. 1964–1979, 2011.
[6]
B. Vahdani and M. Zandieh, “Scheduling trucks in cross-docking systems: robust meta-heuristics,” Computers and Industrial Engineering, vol. 58, no. 1, pp. 12–24, 2010.
[7]
A. R. Boloori Arabani, S. M. T. Fatemi Ghomi, and M. Zandieh, “A multi-criteria cross-docking scheduling with just-in-time approach,” International Journal of Advanced Manufacturing Technology, vol. 49, no. 5–8, pp. 741–756, 2010.
[8]
R. Ruiz and T. Stützle, “A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem,” European Journal of Operational Research, vol. 177, no. 3, pp. 2033–2049, 2007.
[9]
J. M. Framinan and R. Leisten, “A multi-objective iterated greedy search for flowshop scheduling with makespan and flowtime criteria,” OR Spectrum, vol. 30, no. 4, pp. 787–804, 2008.
[10]
G. Minella, R. Ruiz, and M. Ciavotta, “Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems,” Computers and Operations Research, vol. 38, no. 11, pp. 1521–1533, 2011.
[11]
J. Knowles, L. Thiele, and E. Zitzler, “A tutorial on the performance assessment of stochastic multi-objective optimizers,” Tech. Rep. 214, revised version, Computer Engineering and Networks Laboratory (TIK), ETH, Zurich, Switzerland, 2006.
[12]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
[13]
E. Zitzler, J. Knowles, and L. Thiele, “Quality assessment of Pareto set approximations,” in Multi-Objective Optimization: Interactive and Evolutionary Approaches, pp. 373–404, Springer, Berlin, Germany, 2008.
[14]
E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257–271, 1999.
[15]
E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. Da Fonseca, “Performance assessment of multiobjective optimizers: an analysis and review,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 117–132, 2003.
