Non Dominated Sorting Genetic Algorithm for Chance Constrained Supplier Selection Model with Volume Discounts
- 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
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.
KeywordsSupplier selection Chance constrained approach Incremental quantity discount model Genetic algorithms
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