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Stochastic vendor selection problem: chance-constrained model and genetic algorithms

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  • Shiwei He
  • Sohail Chaudhry
  • Zhonglin Lei
  • Wang Baohua

Abstract

We study a vendor selection problem in which the buyer allocates an order quantity for an item among a set of suppliers such that the required aggregate quality, service, and lead time requirements are achieved at minimum cost. Some or all of these characteristics can be stochastic and hence, we treat the aggregate quality and service as uncertain. We develop a class of special chance-constrained programming models and a genetic algorithm is designed for the vendor selection problem. The solution procedure is tested on randomly generated problems and our computational experience is reported. The results demonstrate that the suggested approach could provide managers a promising way for studying the stochastic vendor selection problem. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • Shiwei He & Sohail Chaudhry & Zhonglin Lei & Wang Baohua, 2009. "Stochastic vendor selection problem: chance-constrained model and genetic algorithms," Annals of Operations Research, Springer, vol. 168(1), pages 169-179, April.
    • Handle: RePEc:spr:annopr:v:168:y:2009:i:1:p:169-179:10.1007/s10479-008-0367-5
    DOI: 10.1007/s10479-008-0367-5
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    Cited by:

    1. Shiwei He & Rui Song & Sohail S. Chaudhry, 2014. "Service-oriented intelligent group decision support system: Application in transportation management," Information Systems Frontiers, Springer, vol. 16(5), pages 939-951, November.
    2. Xiangling Hu & Charles Munson & Stergios Fotopoulos, 2012. "Purchasing decisions under stochastic prices: Approximate solutions for order time, order quantity and supplier selection," Annals of Operations Research, Springer, vol. 201(1), pages 287-305, December.
    3. Federico Toffano & Michele Garraffa & Yiqing Lin & Steven Prestwich & Helmut Simonis & Nic Wilson, 2022. "A multi-objective supplier selection framework based on user-preferences," Annals of Operations Research, Springer, vol. 308(1), pages 609-640, January.

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