New Lower Bounds on the Number of Vehicles for the Vehicle Routing Problem with Time Windows
The Vehicle Routing Problem with Time Windows (VRPTW) consists in determining the routing plan of vehicles with identical capacity in order to supply the demands of a set of customers with predefined time windows. This complex multi-constrained problem has been widely studied due to its industrial, economic and environmental implications. In this work, we are interested in defining the number of vehicles needed to visit all the customers. This objective is very important to evaluate the fixed costs for operating the fleet. In this paper, we provide an analysis of several lower bounds based on incompatibility between customers and on vehicle capacity constraints. We also develop an adaptation of Energetic Reasoning algorithm for VRPTW with a limited fleet. The proposed approach focuses on some time-intervals and exploits time constraints, incompatibility graph and bin packing models in order to obtain new valid lower bounds for the fleet size. Experiments conducted on the standard benchmarks show that our algorithms outperform the classical lower bound techniques and give the minimum number of vehicles for 339 out of 468 instances.
Keywordsvehicle routing time windows lower bounds energetic reasoning
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- 7.Cordeau, J.F., Desaulniers, G., Desrosiers, J., Solomon, M.M., Soumis, F.: The vehicle routing problem. ch. VRP with Time Windows, pp. 157–193. Society for Industrial and Applied Mathematics, Philadelphia (2001)Google Scholar
- 9.Erschler, J., Lopez, P., Thuriot, C.: Raisonnement temporel sous contraintes de ressources et problèmes d’ordonnancement. Revue d’Intelligence Artificielle 5(3), 7–36 (1991)Google Scholar
- 11.Gehring, H., Homberger, J.: A parallel hybrid evolutionary metaheuristic for the vehicle routing problem with time windows. In: Proceedings of EUROGEN 1999, vol. 2, pp. 57–64 (1999)Google Scholar
- 13.Golden, B.L., Assad, A.A., Wasil, E.A.: Routing vehicles in the real world: Applications in the solid waste, beverage, food, dairy, and newspaper industries. In: The Vehicle Routing Problem, pp. 245–286. SIAM Monographs on Discrete Mathematics and Applications, Philadelphia (2002)zbMATHCrossRefGoogle Scholar
- 16.Jung, S., Moon, B.R.: A hybrid genetic algorithm for the vehicle routing problem with time windows. In: GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9-13, pp. 1309–1316. Morgan Kaufmann, New York (2002)Google Scholar