Optimization Letters

, Volume 8, Issue 2, pp 753–762 | Cite as

Optimizing two-level reverse distribution networks with hybrid memetic algorithms

  • A. R. R. Freitas
  • V. M. R. Silva
  • F. Campelo
  • F. G. Guimarães
Original Paper

Abstract

In a Two-Level Reverse Distribution Network, products are returned from customers to manufacturers through collection and refurbishing sites. The costs of the reverse chain often overtake the costs of the forward chain by many times. With some known algorithms for the problem as reference, we propose a hybrid memetic algorithm that uses linear programming and a heuristic for defining routes. Moreover, we describe heuristics for deciding locations, algorithms to define routes for the products, and problem-specific genetic operators. Memetic algorithms have returned the best results for all instances.

Keywords

Evolutionary computation Memetic algorithms  Reverse distribution networks Logistics 

References

  1. 1.
    Min, H.: A bicriterion reverse distribution model for product recall. Omega 17(5), 483–490 (1989)CrossRefGoogle Scholar
  2. 2.
    Chandran, R., Lancioni, R.A.: Product recall: a challenge for the 1980s. Int. J. Phys. Distrib. Logist. Manag. 11(8), 46–55, 483–490 (1981)Google Scholar
  3. 3.
    Jayaraman, V., Patterson, R.A., Rolland, E.: The design of reverse distribution networks: models and solution procedures. Eur. J. Oper. Res. 150(1), 128–149 (2003)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Davis, P., Ray, T.: A branch-bound algorithm for the capacitated facilities location problem. Nav. Res. Logist. Quart. 16(3), 331–343 (1969)CrossRefMATHGoogle Scholar
  5. 5.
    Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., van der Laan, E., van Nunen, J.A., Wassenhove, L.N.V.: Quantitative models for reverse logistics: a review. Eur. J. Oper. Res. 103(1), 1–17 (1997)CrossRefMATHGoogle Scholar
  6. 6.
    Hu, T.L., Sheu, J.B., Huang, K.H.: A reverse logistics cost minimization model for the treatment of hazardous wastes. Transp. Res. Part E Logist. Transp. Rev. 38(6), 457–473 (2002)CrossRefGoogle Scholar
  7. 7.
    Sheu, J.B., Chou, Y.H., Hu, C.C.: An integrated logistics operational model for green-supply chain management. Transp. Res. Part E Logist. Transp. Rev. 41(4), 287–313 (2005)CrossRefGoogle Scholar
  8. 8.
    Montané, F.A.T., Galvão, R.D.: A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Comp. Oper. Res. 33(3), 595–619 (2006)CrossRefMATHGoogle Scholar
  9. 9.
    Ko, H.J., Evans, G.W.: A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3pls. Comp. Oper. Res. 34(2), 346–366 (2007)CrossRefMATHGoogle Scholar
  10. 10.
    Alshamrani, A., Mathur, K., Ballou, R.H.: Reverse logistics: simultaneous design of delivery routes and returns strategies. Comp. Oper. Res. 34(2), 595–619 (2007)CrossRefMATHGoogle Scholar
  11. 11.
    Lu, Z., Bostel, N.: A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Comp. Oper. Res. 34(2), 299–323 (2007)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Spengler, T., Püchert, H., Penkuhn, T., Rentz, O.: Environmental integrated production and recycling management. Eur. J. Oper. Res. 97(2), 308–326 (1997)CrossRefMATHGoogle Scholar
  13. 13.
    Bautista, J., Pereira, J.: Modeling the problem of locating collection areas for urban waste management. An application to the metropolitan area of Barcelona. Omega 34(6), 617–629 (2006)CrossRefGoogle Scholar
  14. 14.
    Barros, A., Dekker, R., Scholten, V.: A two-level network for recycling sand: a case study. Eur. J. Oper. Res. 110(2), 199–214 (1998)Google Scholar
  15. 15.
    Horvath, P.A., Autry, C.W., Wilcox, W.E.: Liquidity implications of reverse logistics for retailers: a markov chain approach. J. Retail. 81(3), 191–203 (2005)Google Scholar
  16. 16.
    Freitas, A., Silva, V., Guimarães, F., Campelo, F.: Genetic algorithms applied to reverse distribution networks. In: Snášel, V., Abraham, A., Corchado, E.S. (eds.) Soft computing models in industrial and environmental applications. Advances in intelligent systems and computing, vol. 188, pp. 317–326. Springer, Berlin/Heidelberg (2013)Google Scholar
  17. 17.
    Rosing, K., ReVelle, C.: Heuristic concentration: two stage solution construction. Eur. J. Oper. Res. 97(1), 75–86 (1997)CrossRefMATHGoogle Scholar
  18. 18.
    Freitas, A.R., Guimarães, F.G.: Originality and diversity in the artificial evolution of melodies. In: Proceedings of the 13th annual conference on genetic and evolutionary computation, GECCO ’11. ACM, New York, pp. 419–426 (2011)Google Scholar
  19. 19.
    Whitacre, J.M.: Adaptation and self-organization in evolutionary algorithms. CoRR abs/0907.0516 (2009)Google Scholar
  20. 20.
    Kreinovich, V., Quintana, C., Fuentes, O.: Genetic algorithms: what fitness scaling is optimal? Cybern. Syst. 24(1), 9–26 (1993)CrossRefMATHMathSciNetGoogle Scholar
  21. 21.
    Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 4, 284–294 (2000)CrossRefGoogle Scholar
  22. 22.
    Hsu, J.: Multiple comparisons: theory and methods. Chapman and Hall, London (1996)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • A. R. R. Freitas
    • 1
  • V. M. R. Silva
    • 2
  • F. Campelo
    • 3
  • F. G. Guimarães
    • 3
  1. 1.Graduate Program in Electrical EngineeringUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Instituto de ComputaçãoUniversidade Federal Fluminense NiteróiBrazil
  3. 3.Departamento de Engenharia ElétricaUniversidade Federal de Minas Gerais Belo HorizonteBrazil

Personalised recommendations