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

  • Remica Aggarwal
  • Ainesh Bakshi
Conference paper

DOI: 10.1007/978-3-319-05458-2_48

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8398)
Cite this paper as:
Aggarwal R., Bakshi A. (2014) Non Dominated Sorting Genetic Algorithm for Chance Constrained Supplier Selection Model with Volume Discounts. In: Nguyen N.T., Attachoo B., Trawiński B., Somboonviwat K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science, vol 8398. Springer, Cham

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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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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