Configuring a greener closed-loop supply chain: A case study of an electric vehicle battery manufacturer

Abstract

A closed-loop supply chain configuration (CLSCC) necessitates firms strategically selecting partners at each stage of a closed-loop supply chain (CLSC). The existing literature on CLSCC predominantly emphasizes maximizing overall profitability and focuses on economic sustainability. However, there is a notable gap in addressing CLSCC compliance with carbon emissions regulations and environmental sustainability practices. This study aims to bridge this gap by developing a CLSCC optimization model that seamlessly incorporates emission regulations and policies into the modelling framework. The proposed model is a mixed-integer nonlinear programming (MINLP) optimization model. We utilize the outer approximation/equality relaxation/augmented penalty (OA/ER/AP) algorithm to solve the problem. Furthermore, to validate the model, a case study is conducted using data from an Indian electric vehicle (EV) battery manufacturer. Comprehensive computational experiments and analyses are performed based on parameters obtained from the manufacturer. Additionally, the study conducts a comparative evaluation of three emission-related policy frameworks. Key findings indicate that an incentive-based emissions policy yields the highest net profit while achieving the lowest carbon emissions within the CLSC. Further analysis examines the impact of key variables such as the return rate of used products and CLSC manufacturing capacity under the optimal policy framework. The insights generated from this study hold practical relevance for firms operating within the EV manufacturing sector or those considering market entry.

Publication Title

Journal of the Operational Research Society

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