Abstract
In the Vehicle Routing Problem with Backhauls there are linehaul customers, who demand products, and backhaul customers, who supply products, and there is a fleet of vehicles available for servicing customers. The problem consists in finding a set of routes with the minimum cost, such that all customers are serviced. A generalization of this problem considers the collection from the backhaul customers optional. If the number of vehicles, the cost, and the uncollected demand are assumed to be equally important objectives, the problem can be tackled as a multi-objective optimization problem. In this paper, we solve these as multi-objective problems with an adapted previously proposed evolutionary algorithm and evaluate its performance with proper tools.
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References
Baldacci, R., Bartolini, E., Laporte, G.: Some applications of the generalized vehicle routing problem. J. Oper. Res. Soc. 61(7), 1072–1077 (2010)
Brandão, J.: A new tabu search algorithm for the vehicle routing problem with backhauls. Eur. J. Oper. Res. 173(2), 540–555 (2006)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE T. on Evolut. Comput. 6(2), 182–197 (2002)
Franks, J.: A (Terse) Introduction to Lebesgue Integration. AMS (2009)
Gajpal, Y., Abad, P.L.: Multi-ant colony system (MACS) for a vehicle routing problem with backhauls. Eur. J. Oper. Res. 196(1), 102–117 (2009)
Garcia-Najera, A.: Preserving population diversity for the multi-objective vehicle routing problem with time windows. In: Rothlauf, F. (ed.) Genetic and Evolutionary Computation Conference 2009, pp. 2689–2692. ACM (2009)
Garcia-Najera, A., Bullinaria, J.A.: An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 38(1), 287–300 (2011)
Goetschalckx, M., Jacobs-Blecha, C.: The vehicle routing problem with backhauls. Eur. J. Oper. Res. 42(1), 39–51 (1989)
Laporte, G.: Fifty years of vehicle routing. Transport. Sci. 43(4), 408–416 (2009)
Osman, I.H., Wassan, N.A.: A reactive tabu search meta-heuristic for the vehicle routing problem with back-hauls. J. Sched. 5(4), 263–285 (2002)
Ropke, S., Pisinger, D.: A unified heuristic for a large class of vehicle routing problems with backhauls. Eur. J. Oper. Res. 171(3), 750–775 (2006)
Toth, P., Vigo, D.: A heuristic algorithm for the symmetric and asymmetric vehicle routing problems with backhauls. Eur. J. Oper. Res. 113(3), 528–543 (1999)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN V 1998. LNCS, vol. 1498, pp. 292–304. Springer, Heidelberg (1998)
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Garcia-Najera, A. (2012). The Vehicle Routing Problem with Backhauls: A Multi-objective Evolutionary Approach. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_22
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DOI: https://doi.org/10.1007/978-3-642-29124-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29123-4
Online ISBN: 978-3-642-29124-1
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