Abstract
The Local Pickup and Delivery Problem (LPDP) has drawn much attention, and optimization models and algorithms have been developed to address this problem. However, for real world applications, the large-scale and dynamic nature of the problem causes difficulties in getting good solutions within acceptable time through standard optimization approaches. Meanwhile, actual dispatching solutions made by field experts in transportation companies contain embedded dispatching rules. This paper introduces a Data Mining-based Dispatching System (DMDS) to first learn dispatching rules from historical data and then generate dispatch solutions, which are shown to be as good as those generated by expert dispatchers in the intermodal freight industry. Three additional benefits of DMDS are: (1) it provides a simulation platform for strategic decision making and analysis; (2) the learned dispatching rules are valuable to combine with an optimization algorithm to improve the solution quality for LPDPs; (3) by adding optimized solutions to the training data, DMDS is capable to generate better-than-actuals solutions very quickly.
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References
Bräsy, O., & Gendreau, M. (2005a). Vehicle routing problem with time windows, part I: route construction and local search algorithms. Transportation Science, 39(1), 104–118.
Bräsy, O., & Gendreau, M. (2005b). Vehicle routing problem with time windows, part II: metaheuristics. Transportation Science, 39(1), 119–139.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. (1984). Classification and regression trees. Belmont: Wadsworth.
Campbell, A. M., & Savelsbergh, M. (2004). Efficient insertion heuristics for vehicle routing and scheduling problems. Transportation Science, 38(3), 369–378.
Chan, K. Y., & Loh, W. Y. (2004). Lotus: an algorithm for building accurate and comprehensible logistic regression trees. Journal of Computational and Graphical Statistics, 13(4), 826–852.
Desrosiers, J., Soumis, F., Desrochers, M., & SauvéGerad, M. (1986). Methods for routing with time windows. European Journal of Operational Research, 23(2), 236–245.
Dumas, Y., Desrosiers, J., & Soumis, F. (1991). The pickup and delivery problem with time windows. European Journal of Operational Research, 54(1), 7–22.
Ekins, S., Shimada, J., & Chang, C. (2006). Application of data mining approaches to drug delivery. Advanced Drug Delivery Reviews, 58, 1409–1430.
Fagerholt, K., & Christiansen, M. (2000). A travelling salesman problem with allocation, time window and precedence constraints—an application to ship scheduling. International Transactions in Operational Research, 7(3), 231–244.
Funke, B., Grunert, T., & Irnich, S. (2005). Local search for vehicle routing and scheduling problems: review and conceptual integration. Journal of Heuristics, 11(4), 267–306.
Holsapple, C. W., Lee, A., & Otto, J. (1997). A machine learning method for multi-expert decision support. Annals of Operations Research, 75, 171–188.
Li, X., & Ólafsson, S. (2005). Discovering dispatching rules using data mining. Journal of Scheduling, 8, 515–527.
Lim, A., & Wang, F. (2005). Multi-depot vehicle routing problem: a one-stage approach. IEEE Transactions on Automation Science and Engineering, 2(4), 397–402.
Lim, A., Wang, F., & Xu, Z. (2006). A transportation problem with minimum quantity commitment. Transportation Science, 40(1), 117–129.
Liu, H., & Motoda, H. (1998). Feature extraction, construction and selection: a data mining perspective. Norwell: Kluwer Academic.
Long, W. J., Griffith, J. L., & Selker, H. P. (1993). A comparison of logistic regression to decision-tree induction in a medical domain. Computers and Biomedical Research, 26, 74–97.
Mourkousis, G., Protonotarios, M., & Varvarigou, T. (2003). Application of genetic algorithms to a large-scale multiple-constraint vehicle routing problem. International Journal of Computational Intelligence and Applications, 3(1), 1–21.
Nanry, W. P., & Barnes, J. W. (2000). Solving the pickup and delivery problem with time windows using reactive tabu search. Transportation Research. Part B, 34(2), 107–121.
Pi, L., Pan, Y., & Shi, L. (2008). Hybrid nested partitions and mathematical programming approach and its applications. IEEE Transactions on Automation Science and Engineering, 5(4), 573–586.
Piramuthu, S., Raman, N., & Shaw, M. J. (1998). Decision support system for scheduling a flexible flow system: incorporation of feature construction. Annals of Operations Research, 78, 219–234.
Powell, W. B., & Carvalho, T. (1998). Dynamic control of logistics queueing networks for large scale fleet management. Transportation Science, 32(2), 90–109.
Powell, W. B., Shapiro, J., & Simao, H. P. (2002). An adaptive, dynamic programming algorithm for the heterogeneous resource allocation problem. Transportation Science, 36(2), 231–249.
Quinlan, J. R. (1986). Induction of decision tree. Machine Learning, 1(1), 81–106.
Shaw, M. J., Park, S. C., & Raman, N. (1992). Intelligent scheduling with machine learning capabilities: the induction of scheduling knowledge. IIE Transactions, 24(2), 156–168.
Shi, L., & Ólafsson, S. (2000). Nested partitions method for global optimization. Operations Research, 48(3), 390–407.
Wang, X., & Regan, A. C. (2002). Local truckload pickup and delivery with hard time window constraints. Transportation Research. Part B, 36, 78–94.
Witten, I. H., & Frank, E. (2005). Data mining: practical machine learning tools and techniques (second ed.). San Mateo: Morgan Kaufmann.
Xu, H., Chen, Z. L., Rajagopal, S., & Arunapuram, S. (2001). Solving a practical pickup and delivery problem. Transportation Science, 37(3), 347–364.
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Chen, W., Song, J., Shi, L. et al. Data mining-based dispatching system for solving the local pickup and delivery problem. Ann Oper Res 203, 351–370 (2013). https://doi.org/10.1007/s10479-012-1118-1
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DOI: https://doi.org/10.1007/s10479-012-1118-1