Optimizing Closed-Loop Supply Chain in the Electric Vehicle Battery Industry: A Fully Fuzzy Approach

Increasing vehicle emissions are major causes of global warming which is the most serious threat to human life. To alleviate this process, the Net-zero regulations enforce car manufacturers and encourage the population to shift from gasoline- to Electric vehicles. Although EV usage is unprecedently amplified, existing uncertainty in the supply chain of batteries of electric vehicles (BEVs) endangers EV's future market. For example, the scarcity of battery minerals, and the vagueness of supply chain parameters like costs. Reverse logistics in the BEVs supply chain can cope with the shortage of raw materials, and fuzzy theory is a promising approach to handle the vagueness. This study aims to put forward a fully fuzzy multi-Objective mathematical model by considering the uncertainty to optimize the BEVs closed-loop supply chain according to sustainable development principles in Canada. To do so, three objective functions are developed. Two objective functions maximize the profits of all supply chain players and service levels. The last one minimizes environmental impacts. Eventually, the model obtains the optimal amount of material flow, as decision variables, between all components of the supply chain.