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.
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The authors would like to thank the referees for providing constructive comments that led to an improved version of the paper. Also, this research was partially supported by grants from National Natural Science Foundation (60776825)—China, 863 Programs (2007AA11Z208)—China, Doctorate Foundation (20040004012)—China, Villanova University Research Sabbatical Fall 2006, and the National Science Foundation (0332490)—USA.
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He, S., Chaudhry, S.S., Lei, Z. et al. Stochastic vendor selection problem: chance-constrained model and genetic algorithms. Ann Oper Res 168, 169–179 (2009). https://doi.org/10.1007/s10479-008-0367-5
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DOI: https://doi.org/10.1007/s10479-008-0367-5