Critical Supply Chain Partners Selection under Risk with Trade-off Options

Fang Xinran

Abstract

This study aims to develop a comprehensive optimization model for the selection of supply chain partners to address the challenges of the increasingly complex global supply chain management. Taking a turbocharger foreign trade and production enterprise in Wuxi, China as a case study, the research integrates the Analytic Hierarchy Process (AHP) and mathematical programming methods, particularly Robust Linear Programming (RLP), to handle the uncertainties associated with indicators such as price and quality, thereby providing enterprises with more accurate decision support for supply chain partners selection. The study initially employs the AHP method to establish selection criteria and determine the weights for component suppliers and packaging material suppliers. Subsequently, Robust Linear Programming (RLP) is utilized to conduct robustness analysis under uncertain environments, ensuring that the chosen partners can maintain the stability and efficiency of the supply chain amidst market fluctuations. Additionally, the research explores the selection of dealers and the allocation of product quantities under uncertain market demand by constructing a hypothetical model aimed at maximizing expected profits. In conclusion, the paper summarizes the research findings and suggests future research directions, including the application of big data and artificial intelligence technologies to enhance decision-making intelligence, a focus on the sustainable development and environmental performance of supply chain partners, as well as the study of supply chain flexibility, responsiveness, and the impact of cross-cultural management and geopolitical factors on partner selection.