Closing the loop: Redesigning sustainable reverse logistics network in uncertain supply chains

This paper develops a robust stochastic optimization model for reverse logistics in closed-loop supply chains. By determining the optimal flow of products using a Chance Constrained Robust Stochastic Programming (CCRSP), it is highlighted how the number of plant openings is influenced by the changes in carbon credit price. To assess the model performance, a set of numerical experiments in different sizes are developed and conducted. The effectiveness of the results are then compared to a proposed Heuristic Hybrid Taguchi PSO (HTPSO) solution algorithm, which underlines the effectiveness of the model. A sensitivity analysis on the carbon emission rate is carried out which underlines the role of Carbon Tax Policy. Finally, a real-life case study within the automotive manufacturing industry is carried out by applying the developed robust stochastic model. From a practical standpoint, the model can potentially be employed to meet the carbon credits that are used for handling the different carbon prices and trade scenarios. Also, it provides insights on how to better manage uncertainties, as well as to reduce the overall emissions in supply chains.

Automated summary

This study developed a robust stochastic optimization model for reverse logistics in closed-loop supply chains, demonstrating its effectiveness through numerical experiments and model comparisons. The model has the potential for practical application in real-life case studies.