A weight-consistent model for fuzzy supplier selection and order allocation problem
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A weight-consistent model for fuzzy supplier selection and order allocation problem Sirin Suprasongsin1,2
· Pisal Yenradee1 · Van-Nam Huynh2
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Decision support for Supplier Selection and Order Allocation (SSOA) is an important application area of multiple criteria decision making (MCDM) problems. In Amid et al. (Int J Prod Econ 131(1):139–145, 2011) proposed and developed a weighted maximin model to ensure the weight-consistent solution for SSOA in an MCDM problem under an uncertain environment. Essentially, this model is based on a weight-consistent constraint and a maximin aggregation operator. This paper reanalyzes the weighted maximin model in terms of the weight-consistent constraint, and then proposes a general weight-consistent model for SSOA in MCDM problems under uncertainty. In this paper, two existing models are reviewed and compared with the proposed model. Three datasets with different ranges of fuzzy demand and full factorial patterns of criteria weights are used to test the performances of the related models. The results showed that the proposed model always generates a weight-consistent Pareto-optimal solution in all cases, while the other existing models do not. Keywords Supplier selection and order allocation · Weight-consistent solution · Maximin aggregation operator · Uncertainty
1 Introduction In a supplier selection and order allocation (SSOA) application for multiple criteria decision making (MCDM) problems, main decisions are related to what products are ordered, in what quantities, and from which supplier(s) Aissaoui et al. (2007). Practically, decision makers are often required to select between several alternatives, considering many criteria. In general, the process of SSOA is complex since it involves both qualitative and quantitative criteria.
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Sirin Suprasongsin [email protected] Pisal Yenradee [email protected] Van-Nam Huynh [email protected]
1
Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
2
Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
123
Annals of Operations Research
The process becomes more complicated if it involves uncertain information, e.g., inventory cost, purchasing cost, defective items, and on-time delivery. SSOA under uncertain demand has been extensively studied. Anupindi and Akella (1993) considered the demand uncertainty in a procurement process. Ray and Jenamani (2016) proposed two new algorithms to evaluate suppliers and allocate order quantity under disruption risk with supply and demand uncertainty. Moghaddam (2015) proposed a model with Monte Carlo simulation and fuzzy goal programming for supplier selection in a reverse logistics system. This model captured the uncertainty in customer demand, supplier capacity, and percentage of returned products. Zhang and Zhang (2011) used a Mixed Integer Programming (MIP) and a branch-bound alogorithm to deal with an SSOA problem under stochastic demand. So fa
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