Abstract
In this paper a Memetic Algorithm (MA) is proposed for solving the Vehicles Routing Problem with Time Windows (VRPTW) multi-objective, using a constraint satisfaction heuristic that allows pruning of the search space to direct a search towards good solutions. An evolutionary heuristic is applied in order to establish the crossover and mutation between sub-routes. The results of MA demonstrate that the use of Constraints Satisfaction Technique permits MA to work more efficiently in the VRPTW.
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References
Mitchell, M.: An Introduction to Genetic Algorithms. Massachusetts Institute of Technology Press, London (1999)
Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan (1975)
Alvarenga, G.B., Mateus, G.R., De Tomi, G.: A genetic and set partition two-phase approach for the vehicle routing problem with time Windows. Computers & Operations Research 34(6), 1561–1584 (2007)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley Professional, Reading (1989)
Krasnogor, N., Smith, J.: MAFRA a Java Memetic Algorithm Framework. Intelligent Computer System Centre University of the west of England Bristol, United Kingdom (2000)
Tavakkoli-Moghaddam, R., Saremi, A.R., Ziaee, M.S.: A memetic algorithm for a vehicle routing problem with backhauls. Applied Mathematics and Computation 181, 1049–1060 (2006)
Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of technology,Pasadena, California, USA (1989)
Cheng-Chung, C., Smith, S.F.: A Constraint Satisfaction Approach to Makespan Scheduling. In: Proceedings of the Third International Conference on Artificial Intelligence Planning Systems, Edinburgh, Scotland, pp. 45–52 (1996) ISBN 0-929280-97-0
Solomon, M.M.: Algorithms for vehicle routing and scheduling problems with time window constraints. Operations Research 35(2) (1987)
Garey, M.R., Johnson, D.S.: Computers and intractability, A Guide to the theory of NP-Completeness. W.H. Freeman and Company, New York (2003)
Toth, P., Vigo, D.: The Vehicle Routing Problem. In: Monographs on Discrete Mathematics and Applications, SIAM, Philadelphia (2001)
Thangiah, S.R.: Vehicle Routing with Time Windows using Genetic Algorithms. In: Chambers, L. (ed.) Application Handbook of Genetic Algorithms: New Frontiers, vol. 2, pp. 253–277. CRC Press, Boca Raton (1995)
Tan, K.C., Lee, L.H., Zhu, Q.L., Ou, K.: Heuristics methods for vehicle routing problem with time windows. In: Artificial Intelligence in Engineering, pp. 281–295. Elsevier, Amsterdam (2001)
Zhu, K.Q.: A new Algorithm for VRPTW. In: Proceedings of the International Conference on Artificial Intelligence ICAI 2000, Las Vegas. USA (2000)
Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research 31(12) (2004) 1985-2004
Tan, K.C., Lee, L.H., Ou, K.: Artificial intelligence heuristics in solving vehicle routing problems with time windows constraints. Engineering Applications of Artificial Intelligence 14(6), 825–837 (2001)
Rhalibi, E.A., Kelleher, G.: An approach to dynamic vehicle routing, rescheduling and disruption metrics. IEEE International Conference on Systems, Man and Cybernetics 4, 3613–3618 (2003)
Chin, A., Kit, H., Lim, A.: A new GA approach for the vehicle routing problem. In: Proceedings 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 307–310 (1999)
Tan, K.C., Lee, T.H., Chew, Y.H., Lee, L.H.: IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 1361–1366 (2003)
Castillo, L., Borrajo, D., Salido, M.A.: Planning, Scheduling and Constraint Satisfaction: From Theory to Practice (Frontiers in Artificial Intelligence and Applications), IOS Press, ISBN-10: 1586034847, ISBN-13: 978-1586034849. Spain (2005)
Cruz-Chávez, M.A., Díaz-Parra, O., Hernández, J.A., Zavala-Díaz, J.C., Martínez-Rangel, M.G.: Search Algorithm for the Constraint Satisfaction Problem of VRPTW. In: Proceeding of CERMA 2007, September 25-28, pp. 336–341. IEEE Computer Society, Los Alamitos (2007)
Aho, A.V., Hopcroft, J.E., Ulllman, J.D.: Structure of data and algorithms. Adisson-Wesley Iberoamericana, Nueva Jersey, Nueva York, California, U.S.A (1988) (Spanish)
Wagner, S., Affenzeller, M.: The HeuristicLab Optimization Environment, Technical Report. Institute of Formal Models and Verification, Johannes Kepler University Linz, Austria (2004)
Affenzeller, M.: A Generic Evolutionary Computation Approach Based Upon Genetic Algorithms and Evolution Strategies. Journal of Systems Science 28(2), 59–72 (2002)
Chafekar, D., Xuan, J., Rasheed, K.: Constrained Multi-objective Optimization Using Steady State Genetic Algorithms, Computer Science Departament University of Georgia. In: Athens, Genetic and Evolutionary Computation Conference, GA 30602, USA (2003)
Wagner, S., Affenzeller, M.: SexualGA: Gender-Specifc Selection for Genetic Algorithms. In: Proceedings of the 9th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2005), vol. 4, pp. 76–81 (2005)
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Cruz-Chávez, M.A., Díaz-Parra, O., Juárez-Romero, D., Martínez-Rangel, M.G. (2008). Memetic Algorithm Based on a Constraint Satisfaction Technique for VRPTW. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_37
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DOI: https://doi.org/10.1007/978-3-540-69731-2_37
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