MODELING THE RESILIENT SUPPLIER SELECTION AND OPTIMAL ORDER ALLOCATION CONSIDERING THE VEHICLE ROUTING AND DISRUPTION RISK ASSESSMENT BASED ON THE BAYESIAN NETWORK

Abstract

This study develops a novel framework for designing resilient supply chain networks under uncertainty and disruption. The framework integrates supplier selection, order allocation, and vehicle routing, considering factors like supplier resilience, transportation costs, and demand variability. The Traveling Purchaser Problem (TPP) has been used to model the resilient supplier selection and determine the optimal order allocation. A key contribution is the incorporation of a Bayesian network to model the cascading effects of disruptions, enabling a more comprehensive understanding of supply chain risks. The proposed model also considers the impact of inflation on demand and the trade-off between cost and resilience. The findings indicate a positive correlation between the penalty for unmet demand and the level of customer service. This research has achieved a resilient supply chain and reduced overall costs by making informed decisions regarding supplier’s proposed price, resilience cost, distance, and optimal supply route. Moreover, the proposed model offers a practical solution for supply chain managers facing unexpected disruptions. By utilizing linear programming, alternative suppliers or routes can be quickly identified in the event of natural disasters. The effectiveness of the model is demonstrated through a case study and sensitivity analysis, highlighting its potential to improve supply chain resilience and performance. The Fuzzy C-Means (FCM) clustering technique and Cross Impact Balance (CIB) Analysis are used for scenario reduction. This model makes manufacturers ready for better decision-making and planning when dealing with future risks and uncertainties.