Abstract
In this paper, we consider the standard dynamic and stochastic vehicle routing problem (dynamic VRP) where new requests are received over time and must be incorporated into an evolving schedule in real time.We identify the key features which make the dynamic problem different from the static problem. The approach presented to address the problem is a hybrid method which manipulates the self-organizing map (SOM) neural network similarly as a local search into a population based memetic algorithm, it is called memetic SOM. The approach illustrates how the concept of intermediate structure provided by the original SOM algorithm can naturally operate in a dynamic and real-time setting of vehicle routing. A set of operators derived from the SOM algorithm structure are customized in order to perform massive and distributed insertions of transport demands located in the plane. The goal is to simultaneously minimize the route lengths and the customer waiting time. The experiments show that the approach outperforms the operations research heuristics that were already applied to the Kilby et al. benchmark of 22 problems with up to 385 customers, which is one of the very few benchmark sets commonly shared on this dynamic problem. Our approach appears to be roughly 100 times faster than the ant colony algorithm MACS-VRPTW, and at least 10 times faster than a genetic algorithm also applied to the dynamic VRP, for a better solution quality.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bentley, J.-L., Weide, B.W., Yao, A.C.: Optimal expected-time algorithms for closest point problems. ACM Trans. Math. Softw. 6(4), 563–580 (1980)
Bertsimas, D.J., Levi, S.D.: A New Generation of Vehicle Routing Research: Robust Algorithms, Addressing Uncertainty. Operations Research 44(2), 286–304 (1996)
Christofides, N., Mingozzi, A., Toth, P.: The vehicle routing problem, pp. 315–338. Wiley (1979)
Cochrane, E.M., Beasley, J.E.: The co-adaptive neural network approach to the euclidean travelling salesman problem. Neural Network 16(10), 1499–1525 (2003)
Cordeau, J.-F., Gendreau, M., Hertz, A., Laporte, G.T., Sormany, J.-S.: New heuristics for the vehicle routing problem. In: Langevin, A., Riopel, D. (eds.) Logistics Systems: Design and Optimization, pp. 279–297. Springer, US (2005)
Cordeau, J.-F., Laporte, G., Mercier, A.: A unified tabu search heuristic for vehicle routing problems with time windows. The Journal of the Operational Research Society 52(8), 928–936 (2001)
Metaheuristics in Vehicle Routing. In: Crainic, T.G., Laporte, G. (eds.) Fleet Management and Logistics, pp. 33–56. Kluwer, Boston (1999)
Creput, J.-C., Koukam, A.: Clustering and routing as a visual meshing process. Journal of Information and optimization sciences 28(4), 573–601 (2007)
Creput, J.-C., Koukam, A.: Interactive meshing for the design and optimization of bus transportation networks. Journal of Transportation Engineering 133(9), 529–538 (2007)
Creput, J.-C., Koukam, A.: Self-organization in evolution for the solving of distributed terrestrial transportation problems. In: Prasad, B. (ed.) Soft Computing Applications in Industry. STUDFUZZ, vol. 226, pp. 189–205. Springer, Heidelberg (2008)
Creput, J.-C., Koukam, A.: A memetic neural network for the euclidean traveling salesman problem. Neurocomputing 72, 1250–1264 (2009)
Creput, J.-C., Koukam, A., Hajjam, A.: Self-organizing maps in evolutionary approach for the vehicle routing problem with time windows. International Journal of Computer Science and Network Security 7(1), 103–110 (2007)
Creput, J.-C., Koukam, A., Lissajoux, T., Caminada, A.: Automatic mesh generation for mobile network dimensioning using evolutionary approach. IEEE Trans. Evolutionary Computation 9(1), 18–30 (2005)
Creput, J.-C., Koukam, A.: The memetic self-organizing map approach to the vehicle routing problem. Soft Computing - A Fusion of Foundations, Methodologies and Applications 12, 1125–1141 (2008)
Dongarra, J.: Performance of various computers using standard linear equations software. Technical Report CS-89-85, Department of Computer Science, University of Tennesse, US (2006)
Ergun, O., Orlin, J.B., Steele-Feldman, A.: Creating very large scale neighborhoods out of smaller ones by compounding moves: A study on the vehicle routing problem. MIT Sloan Working Paper No. 4393-02 (October 2002)
Gambardella, L.M., Taillard, É., Agazzi, G.: Macs-vrptw: A multiple colony system for vehicle routing problems with time windows. In: New Ideas in Optimization, pp. 63–76. McGraw-Hill (1999)
Gendreau, M., Laporte, G., Potvin, J.-Y.: Metaheuristics for the capacitated VRP, pp. 129–154. Society for Industrial and Applied Mathematics, Philadelphia (2001)
Ghiani, G., Guerriero, F., Laporte, G., Musmanno, R.: Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies. European Journal of Operational Research 151 (2003)
Glover, F.: Optimization by ghost image processes in neural networks. Computers and Operations Research 21(8), 801–822 (1994); Heuristic, Genetic and Tabu Search
Gonçalves, G., Hsu, T., Dupas, R., Housroum, H.: Une plate-forme de simulation pour la gestion dynamique de tournées de véhicules. Journal Européen des Systèmes Automatisés 41(5), 515–539 (2007)
Helsgaun, K.: An effective implementation of the lin-kernighan traveling salesman heuristic. European Journal of Operational Research 126(1), 106–130 (2000)
Johnson, D., McGeoch, L.: Experimental analysis of heuristics for the stsp. In: Du, D.-Z., Pardalos, P.M., Gutin, G., Punnen, A. (eds.) The Traveling Salesman Problem and Its Variations of Combinatorial Optimization, vol. 12, pp. 369–443. Springer, US (2004)
Kilby, P., Prosser, P., Shaw, P.: Dynamic vrps: a study of scenarios. Technical Report APES-06-1998, University of Strathclyde, UK (1998)
Kohonen, T.: Self-organization and associative memory, 3rd edn. Springer, New York (1989)
Larsen, A., Madsen, O.B.G., Solomon, M.M.: Recent developments in dynamic vehicle routing systems. In: Sharda, R., Voß, S., Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces Series, vol. 43, pp. 199–218. Springer, US (2008)
Mester, D., Braysy, O.: Active-guided evolution strategies for large-scale capacitated vehicle routing problems. Computers and Operations Research 34(10), 2964–2975 (2007)
Montemanni, R., Gambardella, L., Rizzoli, A., Donati, A.: Ant colony system for a dynamic vehicle routing problem. Journal of Combinatorial Optimization 10, 327–343 (2005)
Moscato, P.: A gentle introduction to memetic algorithms. In: Handbook of Metaheuristics, pp. 105–144. Kluwer Academic Publishers (2003)
Preparata, F.P., Shamos, M.I.: Computational geometry: an Introduction. Springer, New York (1985)
Psaraftis, H.N.: Dynamic vehicle routing: Status and prospects. Annals of Operations Research 61, 143–164 (1995)
Psaraftis, H.N.: Dynamic vehicle routing problems, pp. 223–248. Elsevier Science Ltd. (1998)
Reinelt, G.: Tsplib - a traveling salesman problem library. ORSA Journal on Computing 3(4), 376–384 (1991)
Toth, P., Vigo, D.: The granular tabu search and its application to the vehicle-routing problem. INFORMS Journal on Computing 15(4), 333–346 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hajjam, A., Créput, JC., Koukam, A. (2013). From the TSP to the Dynamic VRP: An Application of Neural Networks in Population Based Metaheuristic. In: Alba, E., Nakib, A., Siarry, P. (eds) Metaheuristics for Dynamic Optimization. Studies in Computational Intelligence, vol 433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30665-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-30665-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30664-8
Online ISBN: 978-3-642-30665-5
eBook Packages: EngineeringEngineering (R0)