Abstract
In this paper, we are interested in the minimization of the travel cost of the traveling salesman problem with time windows. In order to do this minimization we use a Nested Rollout Policy Adaptation (NRPA) algorithm. NRPA has multiple levels and maintains the best tour at each level. It consists in learning a rollout policy at each level. We also show how to improve the original algorithm with a modified rollout policy that helps NRPA to avoid time windows violations.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Baker, E.: An exact algorithm for the time-constrained traveling salesman problem. Operations Research 31(5), 938–945 (1983)
Cazenave, T.: Nested Monte-Carlo search. In: IJCAI, pp. 456–461 (2009)
Christofides, N., Mingozzi, A., Toth, P.: State-space relaxation procedures for the computation of bounds to routing problems. Networks 11(2), 145–164 (1981)
Drake, P.: The last-good-reply policy for monte-carlo go. ICGA Journal 32(4), 221–227 (2009)
Dumas, Y., Desrosiers, J., Gelinas, E., Solomon, M.: An optimal algorithm for the traveling salesman problem with time windows. Operations Research 43(2), 367–371 (1995)
Focacci, F., Lodi, A., Milano, M.: A hybrid exact algorithm for the tsptw. Informs Journal on Computing 14(4), 403–417 (2002)
Gendreau, M., Hertz, A., Laporte, G., Stan, M.: A generalized insertion heuristic for the traveling salesman problem with time windows. Operations Research 46(3), 330–335 (1998)
Johnson, D., Papadimitriou, C.: Computational complexity and the traveling salesman problem. Mass. Inst. of Technology, Laboratory for Computer Science (1981)
López-Ibáñez, M., Blum, C.: Beam-ACO for the travelling salesman problem with time windows. Computers & OR 37(9), 1570–1583 (2010)
Pesant, G., Gendreau, M., Potvin, J., Rousseau, J.: An exact constraint logic programming algorithm for the traveling salesman problem with time windows. Transportation Science 32(1), 12–29 (1998)
Potvin, J., Bengio, S.: The vehicle routing problem with time windows part II: genetic search. Informs Journal on Computing 8(2), 165 (1996)
Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Fromman-Holzboog, Stuttgart, German (1973)
Rimmel, A., Teytaud, F., Cazenave, T.: Optimization of the Nested Monte-Carlo Algorithm on the Traveling Salesman Problem with Time Windows. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Drechsler, R., Farooq, M., Grahl, J., Greenfield, G., Prins, C., Romero, J., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Urquhart, N., Uyar, A.Ş. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 501–510. Springer, Heidelberg (2011)
Rimmel, A., Teytaud, F., Teytaud, O.: Biasing Monte-Carlo Simulations through RAVE Values. In: van den Herik, H.J., Iida, H., Plaat, A. (eds.) CG 2010. LNCS, vol. 6515, pp. 59–68. Springer, Heidelberg (2011)
Rosin, C.D.: Nested rollout policy adaptation for monte carlo tree search. In: Walsh, T. (ed.) IJCAI, pp. 649–654. IJCAI/AAAI (2011)
Schwefel, H.: Adaptive Mechanismen in der biologischen Evolution und ihr Einfluß auf die Evolutionsgeschwindigkeit. Interner Bericht der Arbeitsgruppe Bionik und Evolutionstechnik am Institut für Mess-und Regelungstechnik Re 215(3) (1974)
Solomon, M.: Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research 35(2), 254–265 (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cazenave, T., Teytaud, F. (2012). Application of the Nested Rollout Policy Adaptation Algorithm to the Traveling Salesman Problem with Time Windows. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-34413-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34412-1
Online ISBN: 978-3-642-34413-8
eBook Packages: Computer ScienceComputer Science (R0)