Bus Network Scheduling Problem: GRASP + EAs with PISA * Simulation

  • Ana C. Olivera
  • Mariano Frutos
  • Jessica A. Carballido
  • Ignacio Ponzoni
  • Nélida B. Brignole
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5517)

Abstract

In this work a memetic algorithm for the Bus Network Scheduling Problem (BNSP) is presented. The algorithm comprises two stages: the first one calculates the distance among all the pairs of bus stops, and the second one is a MOEA that uses a novel simulation procedure for the calculus of the fitness function. This simulation method was specially developed for the BNSP. The EA used for the second stage was selected between the IBEA, NSGA-II and SPEA2 by means of some PISA tools. As a result of this experimentation, the SPEA2 was preferred since it presents the more spread solution set.

Keywords

Memetic Evolutionary Algorithms Bus-Network Scheduling Problem Optimization PISA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bielli, M., Caramia, M., Carotenuto, P.: Genetic Algorithms in Bus Network Optimization. Transportation Research Part C: Emerging Technologies 10(1), 19–34 (2002)CrossRefGoogle Scholar
  2. 2.
    Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA - A Platform and Programming Language Independent Interface for Search Algorithms. In: Proceeding of Evolutionary Multi-Criterion Optimization, pp. 494–508 (2003)Google Scholar
  3. 3.
    Ceder, A., Wilson, N.H.M.: Bus Network Design. Transportation Research 20(4), 331–344 (1986)CrossRefGoogle Scholar
  4. 4.
    Ceder, A., Israeli, Y.: User and Operator Perspectives in Transit Network Design. Transportation Research Record 1623, 3–7 (1998)CrossRefGoogle Scholar
  5. 5.
    Chang, S.K., Schonfeld, P.M.: Analytical Optimization of Parallel Bus Routes with Time Dependent and Elastic Demand. Transportation Studies Center Working Paper 89-22, University of Maryland, College Park (1989)Google Scholar
  6. 6.
    Conover, W.: Practical Nonparametric Statistics. John Wiley & Sons, New York (1999)Google Scholar
  7. 7.
    Deb, K., Agarwal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Desaulniers, G., Hickman, M.: Public transit. Handbooks in Operations Research and Management Science. In: Laporte, G., Barnhart, C. (eds.) Transportation, vol. 14, pp. 69–127. Elsevier, Amsterdam (2007)CrossRefGoogle Scholar
  9. 9.
    Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation. Discussion and Generalization, Forrest, 416-423 (1993)Google Scholar
  10. 10.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Massachusetts (1989)MATHGoogle Scholar
  11. 11.
    Hansen, M., Jaszkiewicz, A.: Evaluating the quality of approximations to the non-dominated set. Technical University of Denmark (1998)Google Scholar
  12. 12.
    Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. TIK Computer Engineering and Networks Laboratory (2005)Google Scholar
  13. 13.
    Pattnaik, S.B., Mohan, S., Tom, V.M.: Urban Bus Transit Route Network Design Using Genetic Algorithm. Journal of Transportation Engineering 124(4), 368–375 (1998)CrossRefGoogle Scholar
  14. 14.
    Pitsoulis, L.S., Resende, M.G.C.: Greedy Randomized Adaptive Search Procedures. In: Pardalos, P.M., Resende, M.G.C. (eds.) Handbook of Applied Optimization. Oxford University Press, Oxford (2001)Google Scholar
  15. 15.
    Zhao, F.: Simulated Annealing–Genetic Algorithm for Transit Network Optimization. ASCE Journal of Computing in Civil Engineering 20(1), 57–68 (2006)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zitzler, E., Künzli, S.: Indicator-Based Selection in Multiobjective Search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRefGoogle Scholar
  18. 18.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, T., Periaux, P., Fogarty (eds.) Evolutionary Methods for Design, Optimisations and Control, pp. 19–26 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ana C. Olivera
    • 1
    • 4
  • Mariano Frutos
    • 1
    • 2
    • 4
  • Jessica A. Carballido
    • 1
    • 4
  • Ignacio Ponzoni
    • 1
    • 3
    • 4
  • Nélida B. Brignole
    • 1
    • 3
    • 4
  1. 1.Departamento de Ciencias e Ingeniería de la Computación, Email: lidecc@cs.uns.edu.arArgentina
  2. 2.Departamento de IngenieríaArgentina
  3. 3.Planta Piloto Ingeniería Química (PLAPIQUI) Complejo CCT-UAT, CONICETBahía BlancaArgentina
  4. 4.Av. Alem 1253, B8000CPB Bahía BlancaArgentina

Personalised recommendations