A Multi Objective Reverse Logistics Network Design Model Under Carbon Pricing: An Emerging Economy Perspective

Abstract

Reduction of excessive carbon emissions (CEs) is an acute challenge faced by manufacturing companies in operation of their Reverse Logistics (RL) networks, specifically in the developing nations. Hence, companies are realizing the importance of examining the carbon footprint across the network and attaining significant reduction through refurbishing, repair and recycling of their end of use and end of life products. This research contributes to the area of modeling reverse logistic network design problems, by developing a bi-objective optimization model under carbon tax scheme, for integrating strategic decision making at the design phase of the RL network. The carbon emissions due to each of the operational activities are considered and their corresponding costs along with investment in technology are also embedded in the model. The objectives of the model are: (1) profit maximization (2) carbon emissions minimization. The RL network design is configured as a mixed-integer programming problem model and fuzzy programming is utilized as solution methodology for obtaining a trade-off solution. A case of an Indian electrical and electronics remanufacturer is considered. The results furnishes valuable managerial and policy insights; (i) impacts of carbon tax on the strategic level of RL from an emerging economy perspective, (ii) optimal carbon price for maximum carbon emissions reductions per dollar increase in the total cost.