Multi-criteria decision making optimization for reverse supply chains

Abstract

Modern supply chains are typically linear, transforming virgin raw materials into products for end consumers, who then discard them after use to landfills or incinerators. Nowadays, there are major efforts underway to create a circular economy to reduce non-renewable resource use and waste. One important aspect of these efforts is the development of Reverse Supply Chain (RSC) systems to enable a reverse flow of used products from consumers back to manufacturers, where they can be refurbished or remanufactured, to both economic and environmental benefit. This dissertation develops novel multi-objective optimization models to inform RSC system design at multiple levels: (1) strategic planning of facility location and transportation logistics; (2) tactical planning of optimal pricing; and (3) policy planning to account for potential valuation of RSC emissions. First, physical linear programming was applied to evaluate RSC facility placement by determining the quantities of end-of-life products for transport from candidate collection centers to remanufacturing facilities while satisfying cost and capacity criteria. Second, disassembly and remanufacturing processes have received little attention in industrial engineering and process cost modeling literature. The increasing scale of remanufacturing operations, worth nearly $50 billion annually in the United States alone, have made RSC pricing an important subject of research. A non-linear physical programming model for optimization of a pricing policy for remanufactured products that maximizes total profit and minimizes product recovery costs, was examined and solved. Finally, a deterministic equilibrium model was used to determine the effects of internalizing a cost of RSC greenhouse gas (GHG) emissions into optimization models. Changes in optimal facility use, transportation logistics, and pricing/profit margins were all investigated against a variable cost of carbon, using case study system created based on actual data from sites in the Boston area. As carbon costs increase, the optimal RSC system undergoes several distinct shifts in topology as it seeks new cost-minimal configurations. A comprehensive study for quantitative evaluation and performance of the model has been done using Orthogonal Arrays. Results were compared to top-down estimates from economic input-output life cycle assessment (EIO-LCA) models, to contrast remanufacturing GHG emission quantities with those from original equipment manufacturing operations. Introducing a carbon cost of $40/t CO2e increases modeled remanufacturing costs by 2.7%, but also increases original equipment costs by 2.3%. The assembled work advances the theoretical modeling of optimal RSC systems and presents a rare case study of remanufactured appliances.