Abstract
Recently, crossdocking techniques have been successfully applied in responsive supply chain management. However, most researches focused on physical layout of a crossdock, or scheduling operations within a crossdock. In this paper, we study a multi-crossdock transshipment service problem with both soft and hard time windows. The flows from suppliers to customers via the crossdocks are constrained by fixed transportation schedules. Cargos can be delayed and consolidated in crossdocks, and both suppliers and customers have specific hard time windows. In addition to hard time windows, customers also have less-restrictive time windows, called soft time windows. The problem to minimize the total cost of the multi-crossdock distribution network, including transportation cost, inventory handling cost and penalty cost, can be proved to be NP-hard in the strong sense and hence efficient heuristics are desired. We propose two types of meta-heuristic algorithms, called Adaptive Tabu Search and Adaptive Genetic Algorithm, respectively, to solve the problem efficiently. We conduct extensive experiments and the results show that both of them outperform CPLEX solver and provide fairly good solutions within realistic timescales. We also perform sensitivity analysis and obtain a number of managerial insights.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Balamurugan, R., Ramakrishnan, C. V., & Nidur, S. (2008). Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimization. Applied Soft Computing. doi:10.1016/j.asoc.2007.10.022.
Bressloff, P. C. (1995). Stochastic dynamics of reinforcement learning. Network: Computation in Neural Systems, 6(2), 289–307.
Chen, P., Guo, Y. S., Lim, A., & Rodriguesd, B. (2006). Multiple crossdocks with inventory and time windows. Computers & Operations Research, 33(1), 43–63.
Consoli, S., Darby-Dowman, K., Mladenović, N., & Moreno Pérez, J.A. (2009). Greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem. European Journal of Operational Research, 196(2), 440–449.
Donaldson, H., Johnson, E. L., Ratliff, H. D., & Zhang, M. (1999). Schedule-driven crossdocking networks. Research Report, Georgia Institute of Technology.
Fagerholt, K. (2001). Ship scheduling with soft time windows: an optimisation based approach. European Journal of Operational Research, 131(3), 559–571.
Glover, F., & Laguna, M. (1997). Tabu search. Dordrecht: Kluwer Academic.
Gue, K. R. (1999). The effects of trailer scheduling on the layout of freight terminals. Transportation Science, 33(4), 419–428.
Gendreau, M., Guertin, F., Potvin, J. Y., & Séguin, R. (2006). Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Research Part C: Emerging Technologies, 3(14), 157–174.
Hansen, P., Mladenović, N., & Moreno Pérez, J.A. (2010). Variable neighbourhood search: methods and applications. Annals of Operations Research, 175(1), 367–407.
Hashimoto, H., Ibaraki, T., Imahori, S., & Yagiura, M. (2006). The vehicle routing problem with flexible time windows and traveling times. Discrete Applied Mathematics, 154(16), 2271–2290.
Hansen, P., & Mladenovi, N. (2001). Variable neighborhood search: principles and applications. European Journal of Operational Research, 3(130), 449–467.
Ioannou, G., Kritikos, M., & Prastacos, G. (2003). A problem generator-solver heuristic for vehicle routing with soft time windows. The International Journal of Management Science, Omega, 1(31), 41–53.
Keskin, B. B., & Üster, H. (2007). A scatter search-based heuristic to locate capacitated transshipment points. Computers & Operations Research, 34(10), 3112–3125.
Kaelo, P., & Ali, M. M. (2007). Integrated crossover rules in real coded genetic algorithms. European Journal of Operational Research, 176(1), 60–76.
Kim, H. J., & Hooker, J. N. (2002). Solving fixed-charge network flow problems with a hybrid optimization and constraint programming approach. Annals of Operations Research, 115(1–4), 95–124.
Lee, Y. H., Jung, J. W., & Lee, K. M. (2006). Vehicle routing scheduling for cross-docking in the supply chain. Computers & Industrial Engineering, 51(2), 247–256.
Li, H., & Lim, A. (2003). A Metaheuristic for the pickup and delivery problem with time windows. International Journal on Artificial Intelligent Tools, 2(12), 173–186.
Li, Y., Lim, A., & Rodrigues, B. (2004). Crossdocking: JIT scheduling with time windows. Journal of the Operational Research Society, 55(12), 1342–1351.
Lim, A., Miao, Z., Rodrigues, B., & Xu, Z. (2005). Transshipment through crossdocks with inventory and time windows. Naval Research Logistics, 52(8), 724–733.
Miao, Z., Lim, A., & Ma, H. (2009a). Truck dock assignment problem with operational time constraint within crossdocks. European Journal of Operational Research, 192(1), 105–115.
Miao, Z., Yang, F., & Fu, K. (2009b). A hybrid two-stage genetic algorithm for the transshipment problem in crossdocking networks. Working paper. School of Management, Xiamen University, P.R. China.
Michel, L., Shvartsman, A., & Sonderegger, E. (2010). Optimal deployment of eventually-serializable data services. Annals of Operations Research, doi:10.1007/s10479-010-0684-3.
Mladenović, N., & Hansen, P. (1997). Variable neighborhoodsearch. Computers & Operations Research, 11(24), 1097–1100.
Najim, K., Pibouleau, L., & Le Lann, M. V. (1990). Optimization technique based on learning automata. Journal of Optimization Theory and Applications, 64(2), 331–347.
Ratcliff, D. H., van de Vate, J., & Zhang, M. (1999). Network design for load-driven cross-docking systems, Research Report, Georgia Institute of Technology.
Ross, A., & Jayaraman, V. (2008). An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design. Computers & Industrial Engineering, 55(1), 64–79.
Schaffer, B. (1998). Cross docking can increase efficiency. Automatic I.D. News, 14(8), 34–37.
Shaffer, B. (2000). Implementing a successful crossdocking operation. Plant Engineering, 54(3), 128–134.
Sung, S., & Song, H. (2003). Integrated service network design for a cross-docking supply chain network. Journal of the Operational Research Society, 54(12), 1283–1295.
Tsui, L., & Chang, C. (1990). A microcomputer based decision support tool for assigning dock doors in freight yards. Computers & Industrial Engineering, 19(1–4), 309–312.
Tsui, L., & Chang, C. (1992). An optimal solution to a dock door assignment problem. Computers & Industrial Engineering, 23(1–4), 283–286.
Vis, I. F. A., & Roodbergen, K. J. (2008). Positioning of goods in a cross-docking environment. Computers & Industrial Engineering, 54(3), 677–689.
Vansteenwegen, P., Souffriau, W., & Sörensen, K. (2009). Solving the mobile mapping van problem: a hybrid metaheuristic for capacitated arc routing with soft time windows. Computer & Operations Research (In Press). Available online, 18 May.
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(1), 377–396.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Miao, Z., Yang, F., Fu, K. et al. Transshipment service through crossdocks with both soft and hard time windows. Ann Oper Res 192, 21–47 (2012). https://doi.org/10.1007/s10479-010-0780-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-010-0780-4