Optimizing Location, Allocation, Inventory, and Transportation in a Circular Economy Network Using a Multi-Objective Mathematical Model

Social and environmental sustainability have gained importance due to issues like rising global temperature, unsustainable consumption of raw materials, pollution, and scarcity of landfill sites. Governments, businesses, and researchers are collaborating to limit global temperature rise to below 1.5°C. The circular economy concept is gaining traction for achieving social, environmental, and economic benefits. This chapter proposes a multi-facility, multi-period, multi-product, multi-echelon-capacitated, multi-objective circular economy mathematical model. This is solved using the ε-constraint approach and global criterion method to optimize the profit, emissions, and job creation as economic, environmental, and social functions, respectively. Pareto solutions are obtained using the ε-constraint approach, and a preferred solution is obtained using the global criterion method. The “pyomo” library in Python with the “ipopt” solver was used to compute the results. The proposed model was validated through an illustrative example. This mathematical model can aid entrepreneurs and third-party logistics providers in generating and evaluating future 3R scenarios and making informed decisions regarding location, allocation, inventory, and transportation.