New robust optimization models for closed-loop supply chain of durable products: Towards a circular economy

Circular economy (CE), as a way to address sustainable development, emphasizes on maximum utilization of input resources in a regenerative system that gives rise to the reduction in emissions, energy consumption, and waste. The first aim of this paper is to move towards CE through redesigning a closed-loop supply chain (CLSC) network for durable products, which are one of the most widely used category of goods. For this purpose, various recovery facilities are taken into account in CLSC structure; and cost, CO2 emissions, and energy consumption are taken into consideration as multiple objectives. The relevant mixed integer linear programming model is developed to make supplier selection, location-allocation, transportation mode, assembly technology, and recovery level decisions. Given that the strategic and tactical decisions in a CLSC are largely influenced by uncertainties, finding robust solutions is of great importance. The second aim of this study, thus, is to introduce and formulate novel robust optimization models by ways of possibilistic programming and scenario-based stochastic programming. These models are developed in linear terms to cope with different types of uncertainties including randomness and epistemic. Numerical examples are conducted based on some real energy and CO2 data. The results reveal the superiority of the circularity versus linearity in the supply chain. Pareto optimal solutions indicate the negative correlation of cost with energy consumption and CO2 emissions. The findings also show the reasonable performance of the robust models against uncertainty.