Integrated linguistic entropy weight method and multi-objective programming model for supplier selection and order allocation in a circular economy: A case study

Abstract

Green supply chain management is one of the most important strategies to enhance the competitiveness of modern enterprises. Traditional supplier selection methods seldom consider the entire supply chain. Therefore, this paper proposes an integrated model combining the linguistic entropy weight method (LEWM) with multi-objective programming for supplier selection and order allocation in a circular economy. First, a comprehensive system of green supplier evaluation criteria is established, with each criterion clearly defined. Second, the LEWM is used to select qualified green suppliers, whose rankings are calculated. The proposed LEWM can not only select qualified green suppliers but also analyze each supplier’s performance on each evaluation criterion; it can also provide improvement suggestions for suppliers. Third, based on the characteristics of current global supply chains in the automobile manufacturing industry, the order allocation model is established. The order allocation model constructed in this paper has three objectives: total cost minimization, carbon emission minimization, and procurement value maximization. The weighted maximum and minimum operator method is used to solve the model. Finally, the paper introduces how to use the proposed two-phase model to solve the problem of green supplier selection and order allocation, and conducts sensitivity analysis on the model results. The results show that the proposed framework can effectively resolve green supplier selection and order allocation for an automobile manufacture, and verify the scientific validity of the proposed method. This study could help to improve the relationship between companies and potential suppliers, and to improve green product development capabilities and supply chain management quality, thereby enhancing companies’ market competitiveness.