An application of interactive fuzzy optimization model for redesigning supply chain for resilience

Abstract

Supply chain disruptions compel professionals all over the world to consider alternate strategies for addressing these issues and remaining profitable in the future. In this study, we considered a four-stage global supply chain and designed the network with the objectives of maximizing profit and minimizing disruption risk. We quantified and modeled disruption risk as a function of the geographic diversification of facilities called supply density (evaluated based on the interstage distance between nodes) to mitigate the risk caused by disruptions. Furthermore, we developed a bi-criteria mixed-integer linear programming model for designing the supply chain in order to maximize profit and supply density. We propose an interactive fuzzy optimization algorithm that generates efficient frontiers by systematically taking decision-maker inputs and solves the bi-criteria model problem in the context of a realistic example. We also conducted disruption analysis using a discrete set of disruption scenarios to determine the advantages of the network design from the bi-criteria model over the traditional profit maximization model. Our study demonstrates that the network design from the bi-criteria model has a 2% higher expected profit and a 2.2% lower profit variance under disruption than the traditional profit maximization solution. We envisage that this model will help firms evaluate the trade-offs between mitigation benefits and mitigation costs.

A Bi-Objective Optimization Model for a Low-Carbon Supply Chain Network with Risk of Uncertain Disruptions

“…In addition, Bimpikis et al [35] analyzed the optimal structural design of a multi-level supply chain network under supply disruptions by means of game theory. Kungwalsong et al [36] established a model aiming at maximizing profit and supply density with a view to helping enterprises secure revenue in the face of disruptions and solved it by introducing an interactive fuzzy algorithm. Hu et al [37] built a stochastic programming model to explore the design of the aircraft construction supply chain network with supply disruption, providing guidance for supplier selection and managing the supply risk for aircraft production.…”