Optimization of a resilient circular closed-loop supply chain network under uncertainty

Lubricating oils are among the most widely used petroleum fractions since they are used by different machines and vehicles. In addition to cooling the engine and reducing the friction between moving mechanical parts, motor oil also absorbs pollutants such as sludge, peroxides, and debris that are accumulated in the engine. Therefore, used motor oils are the dangerous materials that can have adverse impacts on the environment and living organisms if not properly managed. Hence, for the first time, this article develops a new bi-objective mixed-integer linear programming (BOMILP) model to form a resilient circular closed-loop supply chain network for managing the used motor oils. Moreover, with the aim of achieving advanced sustainability and circularity goals, the proposed model manages collecting, recycling, producing, and purchasing gallons. Moreover, this study applies a scenario-based stochastic programming method to control the demand uncertainty, and provides a novel fuzzy goal programming method to solve the developed BOMILP model. Finally, the data of an Iranian motor oil production company is applied to validate the proposed optimization model and evaluate the performance of the presented multi-objective solution approach. The results derived from implementing the developed optimization model in the real world and conducting the sensitivity analysis process denote the effectiveness and accuracy of the developed optimization model and solution method.