A Bi-Objective Optimization Problem Considering Economic Cost and Carbon Emissions in a Closed-Loop Supply Chain with Commercial Product Returns

The rapid growth of e-commerce has led to a significant increase in product returns, often due to defects, sizing issues, or mismatches between product descriptions and customer expectations. These returns contribute to higher economic costs and elevated carbon emissions. To address these challenges, this work presents a novel joint location-inventory model of a closed-loop supply chain (CLSC) with commercial product returns, aiming to minimize the total economic cost and carbon emissions. A bi-objective mixed-integer nonlinear programming (MINLP) model is developed to study the joint location-inventory problem in a CLSC, considering two categories of product returns: (i) non-defective (new) returns and (ii) defective returns. The CLSC network considered includes one combined manufacturing and repair center (CMRC), multiple combined distribution and return centers (CDRCs) and multiple customer zones. To solve the bi-objective optimization problem, the weighted sum method, [Formula: see text]-constraint method, and augmented [Formula: see text]-constraint (AUGMECON) algorithms are executed. Findings demonstrate that a reduction in defective returns leads to lower economic costs and carbon emissions, highlighting the importance and critical role of improving product quality. The primary contribution of this work lies in the development of a joint location-inventory optimization model that explicitly incorporates commercial product returns and jointly addresses economic and environmental objectives. This study contributes to sustainable supply chain management literature by offering actionable insights for designing environmentally responsible and cost-efficient CLSC networks.