Non Dominated Sorting Genetic Algorithm for Chance Constrained Supplier Selection Model with Volume Discounts

  • Remica Aggarwal
  • Ainesh Bakshi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8398)

Abstract

This paper proposes a Stochastic Chance-Constrained Programming Model (SCCPM) for the supplier selection problem to select best suppliers offering incremental volume discounts in a conflicting multi-objective scenario and under the event of uncertainty. A Fast Non-dominated Sorting Genetic Algorithm (NSGA-II), a variant of GA, adept at solving Multi Objective Optimization, is used to obtain the Pareto optimal solution set for its deterministic equivalent. Our results show that the proposed genetic algorithm solution methodology can solve the problems quite efficiently in minimal computational time. The experiments demonstrated that the genetic algorithm and uncertain models could be a promising way to address problems in businesses where there is uncertainty such as the supplier selection problem.

Keywords

Supplier selection Chance constrained approach Incremental quantity discount model Genetic algorithms 

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References

  1. 1.
    Alonso-Ayuso, A., Escudero, L.F., Garin, A., Ortuno, M.T., Perez, G.: An approach for strategic supply chain planning under uncertainty based on stochastic 0-1 programming. Journal of Global Optimization 26(1), 97–124 (2003)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Amid, A., Ghodsypour, S.H., O’Brien, C.: A weighted additive fuzzy multi-objective model for the supplier selection problem under price breaks in a supply Chain. Int. J. Production Economics 104, 394–407 (2007)CrossRefGoogle Scholar
  3. 3.
    Burke, G.J., Geunes, J., Romeijnb, H.E., Vakharia, A.: Allocating procurement to capacitated suppliers with concave quantity discounts. Operations Research Letters 36(1), 103–109 (2008)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Charnes, A., Cooper, W.: Chance-constrained programming. Management Science 5, 73–79 (1959)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Charnes, A., Cooper, W.: In management models and industrial applications of linear programming, vols. 1-2. Wiley, New York (1961)Google Scholar
  6. 6.
    Charnes, A., Cooper, W.: Deterministic equivalents for optimizing and satisfying under chance constraints. Operations Research 11, 18–39 (1963)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  8. 8.
    Ebrahim, M., Razmi, J., Haleh, H.: Scatter search algorithm for supplier selection and order lot sizing under multiple price discount environment. Advances in Engineering Software 40, 766–776 (2009)CrossRefMATHGoogle Scholar
  9. 9.
    Lu, H., Yen, G.G.: Rank-density-based multi-objective genetic algorithm and benchmark test function study. IEEE Transactions on Evolutionary Computation 7(4), 325–343 (2003)CrossRefGoogle Scholar
  10. 10.
    Oh, K.J., Kim, T.Y., Min, S.: Using genetic algorithm to support portfolio optimization for index fund management. Expert Systems with Applications 28(2), 371–379 (2005)CrossRefGoogle Scholar
  11. 11.
    Rajagopalan, R., Mohan, C.K., Mehrotra, K.G., Varshney, P.K.: Evolutionary Multi-objective crowding algorithm for path computations. In: Proc. Fifth International Conference on Knowledge based Computer Systems, pp. 46–65 (2004)Google Scholar
  12. 12.
    Rezaei, J., Davoodi, M.: Genetic algorithm for inventory lot-sizing with supplier selection under fuzzy demand and costs. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 1100–1110. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Sawik, T.: Single vs. multiple objective supplier selection in a make to order environment. Omega 38(3/4), 203–212 (2010)CrossRefGoogle Scholar
  14. 14.
    Shiromaru, I., Inuiguchi, M., Sakawa, M.: A fuzzy satisfying method for electric power plant coal purchase using genetic algorithms. European Journal of Operations Research 126, 218–230 (2000)CrossRefMATHGoogle Scholar
  15. 15.
    Vergara, F.E., Khouja, M., Michalewicz, Z.: An evolutionary algorithm for optimizing material flow in supply chains. Computers and Industrial Engineering 43, 407–421 (2002)CrossRefGoogle Scholar
  16. 16.
    Xia, Wu: Supplier selection with multiple criteria in volume discount Environments. Omega 35, 494–504 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Remica Aggarwal
    • 1
  • Ainesh Bakshi
    • 2
  1. 1.Department of ManagementBirla Institute of Technology & SciencePilaniIndia
  2. 2.Department of Computer ScienceRutgers New BrunswickUSA

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