Multi-Objective Multi-Period Supplier Selection Problem with Product Bundling

Wadhwa, Vijay

doi:10.1201/b20114-10

Cited by 1 publication

(1 citation statement)

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“…Ozkok and Tiryaki [40] established a compensatory fuzzy approach for solving a multi-objective linear supplier selection problem with multiple items by using cost, service, and quality as objectives. Some latest research work in the vendor selection problem using fuzzy goal programming includes: Pandey et al [41], Mirzaee et al [42], Abbas [43], Sari [44], Wadhwa [45], Kamal et al [46], Shaw [47], Aggarwal et al [48], Islam and Deb [49], Ho [50], Torres-Ruiz and Ravindran [51], Mahmudul Hassan et al [52], Alizadeh and Yousefi [53], Ozkan and Aydin [54], Jia et al [55], Krishankumar et al [56]. Arikan [57] formulated a multi-objective VSP based on price, quality, customer service and delivery criteria, by using Lai and Hwang [58,59], gave fuzzy augmented approach to solve the fuzzy MOVSP.…”

Section: Literature Reviewmentioning

confidence: 99%

The LR-Type Fuzzy Multi-Objective Vendor Selection Problem in Supply Chain Management

et al. 2020

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Vendor selection is an established problem in supply chain management. It is regarded as a strategic resource by manufacturers, which must be managed efficiently. Any inappropriate selection of the vendors may lead to severe issues in the supply chain network. Hence, the desire to develop a model that minimizes the combination of transportation, deliveries, and ordering costs under uncertainty situation. In this paper, a multi-objective vendor selection problem under fuzzy environment is solved using a fuzzy goal programming approach. The vendor selection problem was modeled as a multi-objective problem, including three primary objectives of minimizing the transportation cost; the late deliveries; and the net ordering cost subject to constraints related to aggregate demand; vendor capacity; budget allocation; purchasing value; vendors’ quota; and quantity rejected. The proposed model input parameters are considered to be LR fuzzy numbers. The effectiveness of the model is illustrated with simulated data using R statistical package based on a real-life case study which was analyzed using LINGO 16.0 optimization software. The decision on the vendor’s quota allocation and selection under different degree of vagueness in the information was provided. The proposed model can address realistic vendor selection problem in the fuzzy environment and can serve as a useful tool for multi-criteria decision-making in supply chain management.

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