A novel bi-objective optimization model for an eco-efficient reverse logistics network design configuration

Ecological sustainability has become a top priority for governments and business practitioners because of the massive surge in electronic waste (e-waste). This ecological context has compelled electronic firms to adopt reverse logistics (RL) and to invest in advanced technologies with a focus on sustainable recovery practices and on reducing excessive carbon emissions (CEs). This study proposes a multi-objective mixed integer programming (MOMIP) model for configuring a RL network design; it incorporates multiple products, multiple recovery facilities, multiple processing technologies, and a selection of vehicle types. The novelty of this study lies in its consideration of four green technologies (inspection, dismantling, repair/refurbishing, and recycling facilities), and its efforts to maximize return yield potential in the form of product, components, and material recovery. The main objective of the model is to minimize overall RL network cost and environmental impact due to processing and transportation. Further, a weighted goal programming technique is used to determine efficient solutions and furnish a trade-off among the conflicting objectives. The mathematical model is validated utilizing a real life case study of an electronics manufacturing firm in India. The total RL network cost and emissions are both reduced, and different technologies are selected automatically for the RL processing facilities. The results demonstrate that the return yield increases significantly with the greener technology selections. The study also draws significant implications, specifically that carbon tax regulatory policies aid in significantly reducing carbon emissions to a large extent along with increasing product return yield. Hence, the results will help industrial managers in their strategic and tactical decision making. Their evaluations of recovery options, technology selection, and vehicle selection with regard to economic and ecological impact will permit decision makers to gain valuable managerial insights.