Uncertain remanufacturing reverse logistics network design in industry 5.0: Opportunities and challenges of digitalization

Abstract

Remanufacturing, a crucial step of reverse logistics, focuses on restoring or enhancing the functionality of waste products. The challenge in planning an effective remanufacturing reverse logistics system lies in the uncertainties from various sources. In addition, the evolving industrial landscape in Industry 5.0 necessitates adaptability to technological advancements. This paper proposes an integrated and digitalized architecture for uncertain reverse logistics network design. A fuzzy optimization model is first formulated to identify potential network configurations under varying demand-satisfying and capacity constraints. These solutions are automatically converted and assessed in a dynamic simulation environment with practical operational logic under a set of real-world scenarios. Numerical experiments are performed to validate the method and show the advantages of integrating optimization with dynamic simulation on a digital platform for strategic network planning. The results, built upon previous research, indicate that while initial investments in technology might be substantial, they may lead to long-term reductions in both costs and emissions. Moreover, collaborative decision-making is essential to mitigate potential disruptions and cascading effects. Our research contributes to the development of a novel integrated decision-support architecture and underscores the role of digitalization and Industry 5.0 in future smart and sustainable reverse logistics planning.