Summary
With advances in technology in communication and navigation, the ability to make decisions online (i.e. as time goes by) becomes increasingly important in transportation and logistics. In this chapter, we focus on online decision making in these areas. First, we point out the importance of anticipation when optimizing decision processes online. Anticipation is the possibility to take into account future events and the influence of decisions taken now on those future events. Second, we discuss how computational intelligence (CI) can be used to design approaches that perform anticipation. We illustrate this particular use of CI techniques in two different applications: dynamic vehicle routing (transportation) and inventory management (logistics). In both cases the use of anticipation is found to lead to substantial improvements. This demonstrates our main conclusion that the ability to perform anticipation in online transportation and logistics is very important.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, T.W.: An Introduction to Multivariate Statistical Analysis. Wiley, New York (1958)
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)
Beamon, B.: Supply chain design and analysis: Models and methods. International Journal of Production Economics 55, 281–294 (1998)
Bent, R., Van Hentenryck, P.: Online stochastic and robust optimization. In: Maher, M.J. (ed.) ASIAN 2004. LNCS, vol. 3321, pp. 286–300. Springer, Heidelberg (2004)
Bent, R., Van Hentenryck, P.: Regrets only! Online stochastic optimization under time constraints. In: McGuinness, D.L., Ferguson, G. (eds.) Proceedings of the National Conference on Artificial Intelligence – AAAI 2004, pp. 501–506. AAAI Press, Menlo Park (2004)
Bent, R., Van Hentenryck, P.: Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Operations Research 52(6), 977–987 (2004)
Bent, R., Van Hentenryck, P.: Waiting and relocation strategies in online stochastic vehicle routing. In: Veloso, M.M. (ed.) Proceedings of the International Joint Conference on Artificial Intelligence – IJCAI 2007, Hyderabad, pp. 1816–1821 (2007)
Bosman, P.A.N., Grahl, J., Rothlauf, F.: SDR: A better trigger for adaptive variance scaling in normal EDAs. In: Thierens, D., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference – GECCO 2007. ACM Press, New York (2007)
Bosman, P.A.N., La Poutré, H.: Learning and anticipation in online dynamic optimization with evolutionary algorithms: The stochastic case. In: Thierens, D., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference – GECCO 2007, pp. 1165–1172. ACM Press, New York (2007)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Norwell (2001)
Branke, J., Kaußler, T., Schmidt, C., Schmeck, H.: A multi–population approach to dynamic optimization problems. In: Parmee, I.C. (ed.) Adaptive Computing in Design and Manufacture – ACDM 2000, pp. 299–308. Springer, Berlin (1999)
Branke, J., Mattfeld, D.: Anticipation and flexibility in dynamic scheduling. International Journal of Production Research 43(15), 3103–3129 (2005)
Branke, J., Middendorf, M., Noeth, G., Dessouky, M.: Waiting strategies for dynamic vehicle routing. Transportation Science 39(3), 298–312 (2005)
Chang, H., Givan, R., Chong, E.: Online scheduling via sampling. In: Chien, S., et al. (eds.) Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems – AIPS 2000, pp. 62–71. AAAI Press, Menlo Park (2000)
Ghiani, G., Guerriero, F., Laporte, G., Musmanno, R.: Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies. European Journal of Operational Research 151(1), 1–11 (2004)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learing. Addison Wesley, Reading (1989)
Grötschel, M., Krumke, S.O., Rambau, J. (eds.): Online optimization of large scale systems. Springer, Berlin (2001)
Ichoua, S., Gendreau, M., Potvin, J.-Y.: Exploiting knowledge about future demands for real-time vehicle dispatching. Transportation Science 40, 211–225 (2006)
Kendall, M.G., Stuart, A.: The Advanced Theory Of Statistics, Inference And Relationship, vol. 2. Griffin, London (1967)
Laporte, G., Louveaux, F., Mercure, H.: The vehicle routing problem with stochastic travel times. Transportation Science 26, 161–170 (1992)
Larsen, A.: The Dynamic Vehicle Routing Problem. PhD thesis, Technical University of Denmark, Denmark (2000)
Mercier, L., Van Hentenryck, P.: Performance analysis of online anticipatory algorithms for large multistage stochastic programs. In: Veloso, M.M. (ed.) Proceedings of the International Joint Conference on Artificial Intelligence – IJCAI 2007, Hyderabad, pp. 1979–1984 (2007)
Minner, S.: Multiple-supplier inventory models in supply chain management: A review. International Journal of Production Economics 81(82), 265–279 (2003)
Mitrovic-Minic, S., Krishnamurti, R., Laporte, G.: Double-horizon based heuristics for the dynamic pickup and delivery problem with time windows. Transportation Science B 38, 669–685 (2004)
Mitrovic-Minic, S., Laporte, G.: Waiting strategies for the dynamic pickup and delivery problem with time windows. Transportation Science B 38, 635–655 (2004)
Nahmias, S.: Production and Operations Analysis. Irwin, Homewood (1997)
Savelsbergh, M.: DRIVE: Dynamic routing of independent vehicles. Operations Research 46(4), 474–490 (1998)
Solomon, M.: The vehicle routing problem and scheduling problems with time window constraints. Operations Research 35, 254–265 (1987)
Tatsuoka, M.M.: Multivariate Analysis: Techniques for Educational and Psychological Research. Wiley, New York (1971)
van Hemert, J.I., La Poutré, J.A.: Dynamic routing problems with fruitful regions: Models and evolutionary computation. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 690–699. Springer, Heidelberg (2004)
Verweij, B., Ahmed, S., Kleywegt, A.J., Nemhauser, G., Shapiro, A.: The sample average approximation method applied to stochastic routing problems: A computational study. Computational Optimization and Applications 24(2-3), 289–333 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Bosman, P.A.N., La Poutré, H. (2008). Online Transportation and Logistics Using Computationally Intelligent Anticipation. In: Fink, A., Rothlauf, F. (eds) Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. Studies in Computational Intelligence, vol 144. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69390-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-69390-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69024-5
Online ISBN: 978-3-540-69390-1
eBook Packages: EngineeringEngineering (R0)