Abstract
The Vehicle Routing Problem with Time Windows involves finding the lowest-cost set of routes to deliver goods to customers, which have service time windows, using a homogeneous fleet of vehicles with limited capacity. In this paper, we propose and analyze the performance of an improved multi-objective evolutionary algorithm, that simultaneously minimizes the number of routes, the total travel distance, and the delivery time. Empirical results indicate that the simultaneous minimization of all three objectives leads the algorithm to find similar or better results than any combination of only two objectives. These results, although not the best in all respects, are better in some aspects than all previously published approaches, and fully multi-objective comparisons show clear improvement over the popular NSGA-II algorithm.
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References
Jozefowiez, N., Semet, F., Talbi, E.G.: Multi-objective vehicle routing problems. Eur. J. Oper. Res. 189(2), 293–309 (2008)
Desrochers, M., Desrosiers, J., Solomon, M.: A new optimization algorithm for the vehicle routing problem with time windows. Oper. Res. 40(2), 342–354 (1992)
Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part I: Route construction and local search algorithms. Transport Sci. 39(1), 104–118 (2005)
Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part II: Metaheuristics. Transport. Sci. 39(1), 119–139 (2005)
Bräysy, O., Dullaert, W., Gendreau, M.: Evolutionary algorithms for the vehicle routing problem with time windows. J. Heuristics 10(6), 587–611 (2004)
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)
Tan, K.C., Chew, Y.H., Lee, L.H.: A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Comput. Optim. and Appl. 34(1), 115–151 (2006)
Ombuki, B., Ross, B.J., Hanshar, F.: Multi-objective genetic algorithms for vehicle routing problem with time windows. Appd. Intel. 24(1), 17–30 (2006)
Garcia-Najera, A., Bullinaria, J.A.: Bi-objective optimization for the vehicle routing problem with time windows: Using route similarity to enhance performance. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 275–289. Springer, Heidelberg (2009)
Garcia-Najera, A.: Preserving population diversity for the multi-objective vehicle routing problem with time windows. In: GECCO (Companion) 2009, pp. 2689–2692. ACM, New York (2009)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8(2), 173–195 (2000)
Deb, K., Jain, S.: Running performance metrics for evolutionary multi-objective optimization. KanGAL report 2002004, Indian Institute of Technology (2002)
Solomon, M.M.: Algorithms for the vehicle routing and scheduling problems with time windows constraints. Oper. Res. 35(2), 254–265 (1987)
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Garcia-Najera, A., Bullinaria, J.A. (2010). Optimizing Delivery Time in Multi-Objective Vehicle Routing Problems with Time Windows. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_6
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DOI: https://doi.org/10.1007/978-3-642-15871-1_6
Publisher Name: Springer, Berlin, Heidelberg
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