A two-level decision-support framework for reverse logistics network design considering technology transformation in Industry 4.0: a case study in Norway

Abstract

Reverse logistics network design is a complex decision-making problem that involves the reuse, repair, remanufacturing, and recycling of end-of-life (EOL) under the tradeoff among conflicting objectives. The cutting-edge technologies in Industry 4.0 are now leading to an unprecedented and dynamic transformation of reverse logistics systems, which, however, further complicates the initial network design. In this paper, a two-level decision-support framework combined with both optimization and dynamic simulation is proposed to balance the cost, environmental impact, and service level in smart and sustainable reverse logistics network design under a dynamically evolving and stochastic environment. The results of a real-world case study in Norway show that the method can better support robust strategic decisions, eliminate dominated/near-dominated solutions, and yield holistic performance analyses considering smart reverse logistics transformation. The proposed two-level decision-support framework can better analyze the impact of the technology transformation of Industry 4.0 on reverse logistics systems, while it also provides a fundamental structure for digital reverse logistics twin.