A stochastic mathematical programming approach to resilient supplier selection and order allocation problem: A case study of Iran Khodro supply chain

Suppliers are one of the main sources of vulnerability in supply chains, which can lead to disruption and risk. Thus, resilient supplier selection can ensure enhanced resilience of the supply process, especially in automotive supply chains. The goal of this study is to select a set of resilient suppliers and ensure optimal demand allocation in an automotive supply chain exposed to risk. For this purpose, a bi-objective two-stage stochastic programming model is presented. In contrast to previous mathematical models, our proposed model incorporates a new objective function to consider the supplier's delivery performance as one of the criteria of resilient supplier selection. In addition, the K-means clustering method is used to cluster and decrease the number of disruption scenarios. Due to the uncertainty of demand, a chance-constrained programming approach is utilized in our proposed model. The augmented epsilon-constraint method is implemented to solve the presented model. Finally, sensitivity analysis is carried out to determine the effect of parameter changes on the final results. The research results indicate that contingency planning can reduce the effect of disruption risks. Further to the above, the strategy of implementing supply chain regionalization is important in reducing the effects of environmental disruption. (c) 2023 Sharif University of Technology. All rights reserved.

Automated summary

This study aims to select a set of resilient suppliers and ensure optimal demand allocation in an automotive supply chain. The proposed model and method consider the delivery performance of suppliers and reduce the number of disruption scenarios. The research results indicate that contingency planning and supply chain regionalization can reduce the risk of supply chain disruption.