A regional information-based multi-attribute and multi-objective decision-making approach for sustainable supplier selection and order allocation

Abstract

Abstract Although extant studies proposed various models and frameworks for sustainable supplier selection problems, they paid limited attention to the incorporation of regional economic, social, and environmental factors simultaneously for global supply chain design due to the difficulty in reflecting various dimensions of the global business environment and their associated risks in a decision model. Existing supplier selection models also tend to focus on the formulation of a simplified supply chain structure rather than considering more realistic supply chain operations under multiple sourcing and product designs. To facilitate the complex decision-making process of global supplier selection problems, this study proposes an integrated approach that consists of two phases to effectively reflect the multi-perspectives of global supply chain design for sustainability. The first phase identifies sustainable supplier regions through multi-attribute utility theory, considering four regional sustainability indices for economic and social factors in global business and logistics, to reflect the decision maker's risk attitudes on global business opportunities. In the second phase, a multi-objective integer linear programming model for multiple sourcing and multiple product designs that minimizes economic and environmental objectives is applied to find optimal suppliers and their order quantities in the regions selected from the first phase. The proposed approach is illustrated through a bicycle supplier selection case study. The results show that the multi-objective sustainable decision under a multiple sourcing strategy for different product designs leads to a supply chain that is significantly different from the single-objective non-sustainable decisions. The case study under different decision scenarios shows that a decision maker should hold a balanced perspective under the multi-objective decision environment for sustainable supply chain design.