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Multiobjective Capacitated Arc Routing Problem

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

The Capacitated Arc Routing Problem (CARP) is a very hard vehicle routing problem raised for instance by urban waste collection. In addition to the total route length (the only criterion minimized in the academic problem), waste management companies seek to minimize also the length of the longest trip. In this paper, a bi-objective genetic algorithm is presented for this more realistic CARP, never studied before in literature. Based on the NSGA-II template, it includes two-key features: use of good constructive heuristics to seed the initial population and hybridization with a powerful local search procedure. This genetic algorithm is appraised on 23 classical CARP instances, with excellent results. For instance, for a majority of instances, its efficient solutions include an optimal solution to the academic CARP (minimization of the total route length).

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References

  1. A. Amberg and S. Voß. A hierarchical relaxations lower bound for the capacitated arc routing problem. In R.H. Sprague (Hrsg.), editor, Proceedings of the 35th Annual Hawaï International Conference on Systems Sciences, pages DTIST02:1–10, Piscataway, 2002. IEEE.

    Google Scholar 

  2. J.M. Belenguer and E. Benavent. A cutting plane algorithm for the capacitated arc routing problem. Computers and Operations Research, 2003. To appear.

    Google Scholar 

  3. J.M. Belenguer, E. Benavent, and F. Cognata. Un metaheuristico para el problemade rutas por arcos con capacidades. In Proocedings of the 23th national SEIO meeting, Valencia, Spain, 1997.

    Google Scholar 

  4. C.A. Coello Coello. An updated survey of ga-based multiobjective optimization techniques. ACM Computing Surveys, 32(2):109–143, 2000.

    Article  Google Scholar 

  5. A. Corberan, E. Fernandez, M. Laguna, and R. Martí. Heuristic solutions to the problem of routing school buses with multiple objectives. Journal of the Operational Research Society, 53(4):427–435, 2002.

    Article  MATH  Google Scholar 

  6. K. Deb. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation, 7(3):205–230, 1999.

    Article  Google Scholar 

  7. K. Deb. Multi objective optimization using evolutionary algorithms. Wiley, 2001.

    Google Scholar 

  8. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.

    Article  Google Scholar 

  9. M. Ehrgott and X. Gandibleux. Multiobjective Combinatorial Optimization, volume 52 of International Series in Operations Research and Management Science, pages 369–444. Kluwer, 2002.

    Google Scholar 

  10. B.L. Golden, J.S. DeArmon, and E.K. Baker. Computational experiments with algorithms for a class of routing problems. Computers and Operations Research, 10(1):47–59, 1983.

    Article  MathSciNet  Google Scholar 

  11. B.L. Golden and R.T. Wong. Capacitated arc routing problems. Networks, 11:305–315, 1981.

    Article  MATH  MathSciNet  Google Scholar 

  12. A. Hertz, G. Laporte, and M. Mittaz. A tabu search heuristic for the capacitated arc routing problem. Operations Research, 48(1):129–135, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  13. S.C. Hong and Y.B. Park. A heuristic for bi-objective vehicle routing problem with time windows constraints. International Journal of Production Economics, 62:249–258, 1999.

    Article  Google Scholar 

  14. P. Lacomme, C. Prins, and W. Ramdane-Chérif. Competitive memetic algorithms for arc routing problems. Annals of Operations Research, 2002. To appear. See also Research report LOSI-2001-01, Université de Technologie de Troyes, France.

    Google Scholar 

  15. P. Moscato. New ideas in optimization, chapter Memetic algorithms: a short introduction, pages 219–234. MacGraw-Hill, 1999.

    Google Scholar 

  16. Y.B. Park and C.P. Koelling. An interactive computerized algorithm for multicriteria vehicle routing problems. Computers and Industrial Engineering, 16:477–490, 1989.

    Article  Google Scholar 

  17. A. Riise. Comparing genetic algorithms and tabu search for multi-objective optimization. In Abstract conference proceedings, page 29, Edinburgh, UK, July 2002. IFORS.

    Google Scholar 

  18. W. Sessomboon, K. Watanabe, T. Irohara, and K. Yoshimoto. A study on multiobjective vehicle routing problem considering customer satisfaction with due-time (the creation of pareto optimal solutions by hybrid genetic algorithm). Transaction of the Japan Society of Mechanical Engineers, 1998.

    Google Scholar 

  19. G. Ulusoy. The fleet size and mixed problem for capacitated arc routing. European Journal of Operational Research, 22:329–337, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  20. E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173–195, 2000.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. CNRS, UMR 6158 - LIMOS/Laboratoire d’Informatique, Université Blaise Pascal, Campus Universitaire des Cézeaux, BP 10125, F-63177, Aubière Cedex, France

    P. Lacomme

  2. Laboratoire d’Optimisation des Systèmes Industriels, Université de Technologie de Troyes, 12 Rue Marie Curie, BP 2060, F-10010, Troyes Cedex, France

    C. Prins

  3. CNRS, UMR 8530 - LAMIH/Systèmes de Production, Université de Valenciennes, Le Mont Houy, F-59313, Valenciennes Cedex 9, France

    M. Sevaux

Authors
  1. P. Lacomme
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  2. C. Prins
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  3. M. Sevaux
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Lacomme, P., Prins, C., Sevaux, M. (2003). Multiobjective Capacitated Arc Routing Problem. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_39

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  • DOI: https://doi.org/10.1007/3-540-36970-8_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

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