Synergistic Closed-Loop Supply Chain Network Design by Considering Robustness, Risk: An Automotive Case Study

Abstract

This study proposes a novel network architecture called SYnergistic CLosed-loop Supply Chain Network Design (SYCLSCND), which incorporates antifragility, sustainability, and agility while considering environmental needs, risk, and robustness. Robust Stochastic Optimization (RSO) and weighted value at risk (WVaR) are recommended for coping with risk and robustness. For the first time, this model includes the expected value and WVaR of cost as an objective function. By including Blockchain Technology (BCT), sustainability (including renewable energy and hybrid vehicles for transportation items), agility (paying attention to demand fulfillment limits), and antifragility (flexible capacity), this research enhances the model. The case study is in the automotive industry. As seen in sensitivity analysis, a main model is 3.78% less than without synergistic. Finally, this study examines the impact of varying demand levels, conservatism coefficient, access level functions, and resiliency scores on cost and time computation. Decreasing demand levels make the use of certain technologies impractical and economically unfavorable. Increasing the conservatism coefficient increases cost and time computation. Different access level functions determine the model's risk-seeking or risk-averse nature. Increasing the resiliency score initially does not affect cost but opens new facilities and increases the cost when it reaches 41%. Increasing the scale of the problem exponentially increases cost and time computation.