Sustainable supplier selection and order allocation under demand, supplier availability and supplier grading uncertainties. (March 2022)

• Record Type: Journal Article
• Authors: Hosseini, Zahra Sadat; Flapper, Simme Douwe; Pirayesh, Mohammadali
• Is Part Of: Computers & industrial engineering. Volume 165(2022)
• Publication Date: 2022-03
• Subjects: Sustainable supplier selection -- Order allocation -- Uncertainty supplier availability -- Hybrid BWM-ER method -- Dynamic programming -- Stochastic programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107811
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Multi-period, multi-item, sustainable supplier selection and order allocation problem. Supplier evaluation based on hybrid BWM- ER method. Bi-objective optimization under stochastic availability suppliers and demand. New solution approach based on stochastic and dynamic programming. Application to a real-life case resulting in faster getting better results. Abstract: Sustainable supplier selection is a pressing mater in supply chain management. This paper tackles the main pain points in decision making during supplier selection and order allocation (SSS & OA) under uncertainties for a multi-item, multi-period setting, where each supplier has its own pricing policy. In the first phase, a hybrid BWM-ER method is employed for evaluating and ranking suppliers. In this method, the best worst method (BWM) for determining weights of the sustainability criteria and evidential reasoning (ER) for evaluating suppliers under uncertainty are used. Based on constraints related to demand, capacity, inventory, and allowed shortages in the next phase, a bi-objective mathematical model is presented, to make a trade-off between sustainability and economic cost. The demand is assumed to be stochastic, and there are uncertainties in the availability of suppliers during different periods. The combination of these two uncertainties is studied via a set of scenarios. A new integrated solution approach based on stochastic programming and dynamic programming to solve the bi-objective model under uncertainties is presented. The proposed approach results are compared with the results obtained via revised multi-choice goal programming and the Epsilon constraint method for a real-life case. These comparisons show that by using the new method, better and faster results are obtained. Sensitivity analysis is used to reveal the effect of quantity discounts, uncertainties in suppliers' availability and demand. Results obtained for a real-life case study are presented and discussed.