PRICAI 2004: PRICAI 2004: Trends in Artificial Intelligence pp 95-103 | Cite as
Indexing Approach for Delivery Demands with Time Constraints
Abstract
Demand-bus system is focused as a new transportation system. Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) we address is a simple environment model for demand-bus system. In the problem, delivery demands with time constraints occur enduringly. Share-ride vehicles transport customers to their destination. In order to solve this problem, we propose CRTPR-Tree which indexes moving vehicles on a road network. A node of the tree consists of a pointer to vehicle (in leaf nodes) or pointers to child nodes (in intermediate nodes), a bounding rectangle, and a time constraint. Moreover, we propose two scheduling algorithms based on time traveling measure (TTM) or time constraint measure (TCM) for delivery orders of customers. We performed experiments with the profitability and the usability on an ideal environment. The experimental results show that our approach produces good effects.
Keywords
Time Constraint Road Network Customer Satisfaction Leaf Node Intermediate NodePreview
Unable to display preview. Download preview PDF.
References
- 1.Desrochers, M., Lenstra, J., Savelsbergh, M.: F.Soumis: Vehicle routing with time windows: Optimizatin and approximation. Vehicle Routing: Methods and Studies, 65–84 (1988)Google Scholar
- 2.Solomon, M., Desrosiers, J.: Time window constrained routing and scheduling problems. Transportations Science 22, 1–13 (1988)MATHMathSciNetCrossRefGoogle Scholar
- 3.Thangiah, S.: Vehicle routing with time windows using genetic algorithms. In: Chambers, L. (ed.) Application Handbook of Genetic Algorithms: New Frontiers, vol. II, pp. 253–277. CRC Press, Boca Raton (1995)Google Scholar
- 4.Potvin, J.Y., Bengio, S.: The vehicle routing problem with time windows — part II: Genetic search. INFORMS Journal on Computing 8, 165–172 (1996)MATHCrossRefGoogle Scholar
- 5.Louis, S.J., Yin, X., Yuan, Z.Y.: Multiple vehicle routing with time windows using genetic algorithms. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of the Congress on Evolutionary Computation, Mayflower Hotel, Washington D.C., USA, vol. 3, pp. 1804–1808. IEEE Press, Los Alamitos (1999)Google Scholar
- 6.Ibaraki, T., Kubo, M., Masuda, T., Uno, T., Yagiura, M.: Effective local search algorithms for the vehicle routing problem with general time window constraints. In: Proc. of MIC 2001, pp. 293–297 (2001)Google Scholar
- 7.Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proc. of ACM SIGMOD 2000, pp. 331–342 (2000)Google Scholar
- 8.Procopiuc, C., Agarwal, P., Har-Peled, S.: Star-tree: An efficient self-adjusting index for moving objects. In: Mount, D.M., Stein, C. (eds.) ALENEX 2002. LNCS, vol. 2409, pp. 178–193. Springer, Heidelberg (2002)CrossRefGoogle Scholar
- 9.Saltenis, S., Jensen, C.S.: Indexing of moving objects for location-based services. In: Proc. of ICDE 2002, pp. 463–473 (2002)Google Scholar
- 10.Tao, Y., Papadias, D., Sun, J.: The TPR∗-tree: An optimized spatio-temporal access method for predictive queries. In: Proc. of Very large data bases, pp. 9–12 (2003)Google Scholar
- 11.Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proc. of ACM SIGMOD 1984, pp. 47–57 (1984)Google Scholar