Abstract
This paper addresses a variant of the vehicle routing problem with time windows in which each customer has two time windows associated. A hard time window indicates the period of time in which the delivery has to take place and a soft time window which lays out the preferences of the customer. In these problems, for the distribution company is important not only minimizing total travel time but satisfying customer preferences. Assuming that vehicles are allowed to wait, we must decide the order in which customers are served and the delivery start time at every customer. Both decisions determine the feasibility of routes and the satisfaction of customers. For solving this problem an Ant Colony System is developed. To deal with delivery preferences, it is assigned to every customer artificial beacons to indicate delivery start time. Drops of pheromone are put on the beacons to guide ants when building routes.
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References
Laporte, G.: Fifty years of vehicle routing. Transportation Science 43(4), 408–416 (2009)
Cordeau, J.F., Gendreau, M., Laporte, G., Potvin, J.Y., Semet, F.: A guide to vehicle routing heuristics. J. of the Operational Research Society 53(5), 512–522 (2002)
Cordeau, J.F., Laporte, G., Savelsbergh, M.W.P., Vigo, D.: Vehicle routing. In: Barnhart, C., Laporte, G. (eds.) Handbook in Operations Research and Management Science, vol. 14, ch. 6, pp. 367–428. Elsevier (2007)
Toth, P., Vigo, D.: The vehicle routing problem. SIAM Monographs on Discrete Mathematics and Applications, Philadelphia, USA (2002)
Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part I: Route construction and local search algorithms. Transportation Science 39(1), 104–118 (2005)
Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part II: Metaheuristics. Transportation Science 39(1), 119–138 (2005)
Min, H.: A multiobjective vehicle routing problem with soft time windows: The case of a public library distribution system. Socio- Economic Planning Science 25(3), 179–188 (1991)
Calvete, H.I., Galé, C., Oliveros, M.J., Sánchez-Valverde, B.: A goal programming approach to vehicle routing problems with soft time windows. European J. of Operational Research 177(3), 1720–1733 (2007)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambrigde (2004)
Dorigo, M., Stützle, T.: Ant colony optimization: Overview and recent advances. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, 2nd edn., pp. 227–263. Springer (2010)
Bullnheimer, B., Hartl, R.F., Strauss, C.: Applying the ant system to the vehicle routing problem. In: Voß, S., Martello, S., Osman, I.H., Roucairol, C. (eds.) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pp. 109–120. Kluwer, Boston (1998)
Bullnheimer, B., Hartl, R.F., Strauss, C.: An improved ant system algorithm for the vehicle routing problem. Annals of Operations Research 89, 319–328 (1999)
Bell, J.E., McMullen, P.R.: Ant colony optimization techniques for the vehicle routing problem. Advanced Engineering Informatics 18, 41–48 (2004)
Gambardella, L., Taillard, E.D., Agazzi, G.: Macs-vrptw: A multiple ant colony system for vehicle routing problems with time windows. In: Corne, F.G.D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 63–76. McGraw Hill, London (1999)
Balakrishnan, N.: Simple heuristics for the vehicle routing problem with soft time windows. J. of the Operational Research Society 44(3), 279–287 (1993)
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Calvete, H.I., Galé, C., Oliveros, MJ. (2012). Ant Colony Optimization for Solving the Vehicle Routing Problem with Delivery Preferences. In: Engemann, K.J., Gil-Lafuente, A.M., Merigó, J.M. (eds) Modeling and Simulation in Engineering, Economics and Management. MS 2012. Lecture Notes in Business Information Processing, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30433-0_23
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DOI: https://doi.org/10.1007/978-3-642-30433-0_23
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