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Computación y Sistemas

versão On-line ISSN 2007-9737versão impressa ISSN 1405-5546

Comp. y Sist. vol.13 no.3 Ciudad de México Jan./Mar. 2010

 

Artículos

 

Evolutionary Algorithm for the Vehicles Routing Problem with Time Windows Based on a Constraint Satisfaction Technique1

 

Algoritmo Evolutivo para el Problema de Ruteo de Vehículos con Ventanas de Tiempo Basado en una Técnica de Satisfacción de Restricciones1

 

Marco Antonio Cruz Chávez* and Ocotlán Díaz Parra**

 

CIICAp, Autonomous University of Morelos State Av. Universidad 1001. Col. Chamilpa, C.P. 6221O.Cuernavaca, Morelos, México. *mcruz@uaem.mx, **odiazp@uaem.mx

 

Article received on April 22, 2008
Accepted on November 10, 2008

 

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 that represent the individuals of the population. 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.

Keywords: Memetic algorithm (GA–PCP), Constraints Satisfaction Problem, Precedence Constraint Posting, local search, VRPTW.

 

Resumen

En este documento se propone un Algoritmo Memetico (MA) para resolver el problema de ruteo vehicular con ventanas de tiempo (VRPTW) multi–objetivo, usando una heurística de satisfacción de restricciones que permite podar el espacio de búsqueda para dirigir la búsqueda hacia buenas soluciones las cuales son representadas por los individuos de la población. Se aplica una heurística evolutiva para establecer el cruzamiento y mutación entre sub–rutas. El resultado del MA demuestra que el uso de la Técnica de Satisfacción de Restricciones permite al MA trabajar más eficientemente en el VRPTW.

Palabras clave: Algoritmo Memetico (GA–PCP), Problema de Satisfacción de Restricciones, Estableciendo Restricciones de Precedencia, Búsqueda Local, VRPTW.

 

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References

1. Adeli, H. & Hung, S. (1995). Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems. New York: John Wiley & Sons.        [ Links ]

2. Affenzeller, M. (2002). A Generic Evolutionary Computation Approach Based Upon Genetic Algorithms and Evolution Strategies. Journal of Systems Science, 28(2), 59–72.        [ Links ]

3. Aho, A.V., Hopcroft, J.E. & Ulllman, J.D. (1988). Structure of data and algorithms. New Jersey: Adisson–Wesley        [ Links ]

4. Alvarenga, G.B., Mateus, G.R. & DeTomi, G. (2007). A genetic and set partition two–phase approach for the vehicle routing problem with time Windows. Computers & Operations Research, 34(6), 1561–1584.        [ Links ]

5. Castillo, L., Borrajo, D. & Salido, M.A. (2005). Planning, Scheduling and Constraint Satisfaction: From Theory to Practice. Frontiers in Artificial Intelligence and Applications, 117(1), 1–10.        [ Links ]

6. Chafekar, D., Xuan, J. & Rasheed, K. (2003). Constrained Multi–objective Optimization Using Steady State Genetic Algorithms. The Genetic and Evolutionary Computation Conference (GECCO'2003). Athens, Greece, 2723, 813–824.        [ Links ]

7. Cheng, C. & Smith, S.F. (1996). A Constraint Satisfaction Approach to Makespan Scheduling. 3th International Conference on Artificial Intelligence Planning Systems, Edinburgh, Scotland, 29–31.        [ Links ]

8. Chin, A., Kit, H. & Lim, A. (1999). A new GA approach for the vehicle routing problem. 11th IEEE International Conference on Tools with Artificial Intelligence, Illinois, USA, 307–310.        [ Links ]

9. Coley, D.A. (1997). An Introduction to Genetic Algorithms for Scientists and Engineers. Singapore: World Scientific Publishing Company.        [ Links ]

10. Cruz–Chávez, M. A., Díaz–Parra, O., Hernández, J. A., Zavala–Díaz, J. C, Martínez–Rangel, M. G. (2007). Search Algorithm for the Constraint Satisfaction Problem of VRPTW. CERMA 2007, 0(1), 336–341.        [ Links ]

11. Dorronsoro–Diaz, B. & Alba, E. (2008). Cellular Genetic algorithms. Luxembourg: Springer–Verlag.        [ Links ]

12. ELRhalibi, A. & Kelleher, G. (2003). An approach to dynamic vehicle routing, rescheduling and disruption metrics. IEEE Interntional Conference on Systems, Man and Cybernetics, 4, 3613–3618.        [ Links ]

13. Garey, M.R. & Johnson, D.S. (2003). Computers and intractability. A Guide to the theory of NP–Completeness. New York: W.H. Freeman and Company.        [ Links ]

14. Gen, M. & Cheng, R. (2000). Genetic Algorithms and Engineering Optimization. Canada: John Wiley and Sons.        [ Links ]

15. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Canada: Addison Wesley Professional.        [ Links ]

16. Holland, J. (1975). Adaptation in Natural and Artificial Systems. Michigan: The University of Michigan Press.        [ Links ]

17. Krasnogor, N. & Smith, J. (2000). MAFRA a Java Memetic Algorithm Framework. Workshops Proceedings of the 2000 International Genetic and Evolutionary Computation Conference (GECCO2000), Las Vegas, USA, 125–130.        [ Links ]

18. Lahoz–Beltra, R. (2004). Bioinformática: Simulación, vida artificial e inteligencia artificial. Madrid: Ediciones Díaz de Santos.        [ Links ]

19. Mitchell, M. (1996). An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press.        [ Links ]

20. Moscato, P. (1989). On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, California, USA.        [ Links ]

21. Moscato, P. & Cotta, C. (2003). An Introduction to Memetic Algorithms. Revista Iberoamericana de Inteligencia artificial 19(2), 131–148.        [ Links ]

22. Potvin, J. Y., Duhamel, C. & Guertin, F. (1996). Genetic Algorithms for vehicle routing with backhauling. Applied Intelligence. 6(4), 345–355.        [ Links ]

23. Prins, C. (2004). A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research. 31(12), 1985–2002.        [ Links ]

24. Smith, D. & Wiley, J. (1982). Network Optimization Practice. A Computational Guide. New York: John Wiley & Sons.        [ Links ]

25. Solomon, M.M. (1987). Algorithms for Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Research, 35(2), 254–265.        [ Links ]

26. Tan, K.C., Lee, L.H., Ou, K. (2001). Artificial intelligence heuristics in solving vehicle routing problems with time windows constraints. Engineering Applications of Artificial Intelligence, 14(6), 825–837.        [ Links ]

27. Tan, K.C., Lee, L.H., Zhu, Q.L., Ou, K. (2001). Heuristics methods for vehicle routing problem with time windows. Artificial Intelligence in Engineering, 15(3), 281–295.        [ Links ]

28. Tan, K.C., Lee, T.H., Chew, Y.H., Lee, L.H. (2003). A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. IEEE International Conference on Systems, 1(1), 361 – 366.        [ Links ]

29. Tavakkoli–Moghaddam, R., Saremi A.R., Ziaee M.S. (2006). A memetic algorithm for a vehicle routing problem with backhavis. Applied Mathematics and Computation, 181(2), 1049–1060.        [ Links ]

30. Tavares, J. and Pereira, F. B. and Machado, P. and Costa, E. (2003). Crossover and Diversity: A Study about GVR. Analysis and Design of Representations and Operators (ADoRo'2003), Genetic and Evolutionary Computation Conference (GECCO–2003), Chicago, Illinois USA, 27–33.        [ Links ]

31. Thangiah, S.R. (1995). Vehicle Routing with Time Windows using Genetic Algorithms. In L. Chambers, (Ed.), Practical Handbook of Genetic Algorithms, Vol. 2. New Frontiers, 253–277'. Boca Raton, Fla.: CRC Press.        [ Links ]

32. Toth, P. & Vigo, D. (2001). The Vehicle Routing Problem. Monographs on Discrete Mathematics and Applications. Philadelphia: SIAM.        [ Links ]

33. Wagner, S. & Affenzeller, M. (2005). SexualGA: Gender–Specifc Selection for Genetic Algorithms. 9th World Multi–Conference on Systemics, Cybernetics and Informatics (WMSCI), 4, 76–81.        [ Links ]

34. Wagner, S. & Affenzeller, M. (2004). The HeuristicLab Optimization Environment. Linz, Austria: Johannes Kepler University.        [ Links ]

35. Zhu, K.Q. (2000). A new Algorithm for VRPTW. Proceedings of the International Conference on Artificial Intelligence IC–AI2000. Las Vegas, USA, 311–320.        [ Links ]

 

Notas

1 This work was supported by project 160 of the Fideicomiso SEP–UNAM, 2006–2007.

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