Advertisement

Hybrid Genetic Algorithm Optimisation of Distribution Networks—A Comparative Study

  • Romeo Marin Marian
  • Lee Luong
  • Son Duy Dao
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 110)

Abstract

This chapter focuses on the second of a three-stage, integrated methodology for modeling and optimising distribution networks (DN) based on hybrid genetic algorithms (HGA). The methodology permits any combination of transportation and warehousing costs for deterministic/stochastic demand. This chapter analyses and compares the fluctuation of overall costs when the number of facilities varies and indicates how to minimize them. The chapter concentrates on capacitated location allocation of distribution centers, a large scale, highly constrained, NP-hard, combinatorial problem. The HGA used has a classical structure, but incorporates a special encoding of solutions as chromosomes and integrates linear programming/mixed integer programming modules in the genetic operators (GO). A complex and extensive case study is described, demonstrating the robustness of the HGA and the optimization approach.

Keywords

Capacitated location-allocation problem Distribution network Hybrid genetic algorithms Optimisation 

References

  1. 1.
    Simchi-Levi D, Kaminsky P, Simchi-Levi E (2008) Designing and managing the supply chain: concepts, strategies and case studies. McGraw-Hill, New YorkGoogle Scholar
  2. 2.
    Fahimnia B, Marian R, Motevallian B (2009) Analysing the hindrances to the reduction of manufacturing lead-time and their associated environmental pollution. Int J Environ Technol Manage 10(1):16–25CrossRefGoogle Scholar
  3. 3.
    Marian RM, Luong LHS, Akararungruangkul R (2008) Optimisation of distribution networks using genetic algorithms - part 1 - Problem modeling and automatic generation of solutions. Int J Manufact Technol Manage 15(Special issue on: Latest concepts and approaches in logistics engineering and management):64–83Google Scholar
  4. 4.
    Marian RM, Luong LHS, Akararungruangkul R (2008) Optimisation of distribution networks using genetic algorithms - Part 2 - The genetic algorithm and genetic operators. Int J Manufact Technol Manage 15(Special issue on: Latest concepts and approaches in logistics engineering and management):84–101Google Scholar
  5. 5.
    Perez M, Almeida F, Moreno-Vega JM (1998) Genetic algorithm with multistart search for the p-Hub median problem. In: 24th Euromicro Conference, VasterasGoogle Scholar
  6. 6.
    Fahimnia B, Luong L, Marian R (2009) Optimization of a two-Echelon supply network using multi-objective genetic algorithms in 2009 World Congress on Computer Science and Information Engineering, Los Angeles, USA, March 31–April 2, 2009Google Scholar
  7. 7.
    Lai KT et al (2008) An optimisation framework for the design of multi-echelon production-distribution networks using genetic algorithms. Asian Int J Sci Technol (AIJSTPME) Product Manufact Eng 1(2):17–29Google Scholar
  8. 8.
    Marian RM, Luong LHS, Abhary K (2006) A genetic algorithm for the optimisation of assembly sequences. Comput Ind Eng 50:503–527CrossRefGoogle Scholar
  9. 9.
    Gen M, Cheng R (1997) Genetic algorithms and engineering design. In: Parsaei H (ed.) Wiley series in engineering design and automation. Wiley, New YorkGoogle Scholar
  10. 10.
    Gen M, Cheng R (2000) Genetic algorithms and engineering optimisation. Wiley, New YorkGoogle Scholar
  11. 11.
    Jayaraman V, Pirkul H (2001) Planning and coordination of production and distribution facilities for multiple commodities. Eur J Oper Res 133:394–408CrossRefMATHGoogle Scholar
  12. 12.
    Eben-Chaime M, Mehrez A, Markovich G (2002) Capacitated location-allocation problems on a line. Comput Oper Res 29(5):459–470CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Silva MR, Cunha CB (2009) New simple and efficient heuristics for the uncapacitated single allocation hub location problem. Comput Oper Res 36(12):3152–3165CrossRefMATHGoogle Scholar
  14. 14.
    Min H, Ko HJ, Ko CS (2006) A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega 34(1):56–69CrossRefGoogle Scholar
  15. 15.
    Chen J-F (2007) A hybrid heuristic for the uncapacitated single allocation hub location problem. Omega 35(2):211–220CrossRefGoogle Scholar
  16. 16.
    Topcuoglu H et al (2005) Solving the uncapacitated hub location problem using genetic algorithms. Comput Oper Res 32:967–984CrossRefMATHGoogle Scholar
  17. 17.
    Sha DY, Che ZH (2006) Supply chain network design: partner selection and production/distribution planning using a systematic model. J Oper Res Soc 57(1):52–62CrossRefMATHGoogle Scholar
  18. 18.
    Hoffmann CM (1989) The problems of accuracy and robustness in geometric computation. IEEE Comput 22(3):31–41CrossRefGoogle Scholar
  19. 19.
    Marian RM, Luong LHS, Abhary K (2003) Assembly sequence planning and optimisation using genetic algorithms. Part I: Automatic generation of feasible assembly sequences. Appl Soft Comput (2/3F):223–253Google Scholar
  20. 20.
    Marian RM, Luong LHS, Dao SD (2011) Modeling and optimisation of distribution networks using hybrid genetic algorithms. A comparative study – The 2011 IAENG international conference on artificial intelligence and applications, Hong Kong, 16–18 March 2011Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of Advanced Manufacturing & Mechanical EngineeringUniversity of South AustraliaMawson LakesAustralia

Personalised recommendations