Stochastic Modeling of Resilient Supplier Selection and Order Allocation Under Conditions of War, Sanctions, and Pandemic: An Analysis Using Bayesian Networks

Document Type : Research Paper

Authors

1 PhD Candidate, Department of Industrial Engineering, Faculty of Industrial Engineering and Management, Malek Ashtar University of Technology, Tehran, Iran

2 Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering and Management, Malek Ashtar University of Technology, Tehran, Iran

Abstract

Disruptions such as war, sanctions, and pandemics not only impact suppliers and manufacturers but also influence each other at the beginning of the supply chain or affect customer demand at the end of the chain. This study employs Bayesian networks to model these complex relationships and demonstrate the extent of disruptions at each point in the supply chain. Inflation rates are utilized to predict and mitigate demand uncertainties. The reliability of suppliers, a critical aspect in supply chains, is incorporated into a bi-objective stochastic mixed-integer programming model with objectives of increasing geographical dispersion and reducing total costs (including transportation, purchasing, and ordering costs). In this model, suppliers and manufacturers collaborate to enhance supply chain resilience. For the first time, the concept of supplier resilience level is introduced. The proposed model for order allocation considers not only prices and other ordering costs but also the costs of improving suppliers' resilience levels. Additionally, customer satisfaction is implicitly calculated by reducing the cost of unmet demand. To validate the model, a case study was conducted at an automotive company in Iran, followed by a numerical example and sensitivity analysis. Scenario reduction was achieved using the fuzzy c-means clustering method and balanced impact analysis. The proposed model equips manufacturers with better decision-making and planning capabilities in the face of future risks and uncertainties.

Main Subjects

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Journal of Industrial Engineering Research in Production Systems
Volume 12, Issue 24
September 2024
Pages 93-113

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