About the Project

Digital Reverse Logistics Twin 5.0 (DigiRL 5.0) explores how to develop human-centric and sustainable reverse logistics systems in Industry 5.0 to enhance decision-making and recycling operations. The project develops a Digital Reverse Logistics Twin, a system-level modeling with prediction, optimization, simulation, and scenario analysis to support reverse logistics network design, vehicle routing, and human-in-the-loop recycling planning and operations. The project emphasizes the role of human expertise in collaboration with digital technologies and analytics, enabling more efficient, flexible, and sustainable management of end-of-life products.

Through interdisciplinary research combining sustainable reverse logistics, analytical modeling, simulation, and human-centered process re-design, DigiRL 5.0 aims to advance theoretical understanding and develop a framework for human-centric digital reverse logistics systems to support planning decisions, minimize carbon footprints, and promote the human-in-the-loop system redesign.

• WP1 – Conceptual Framework
  Development of the conceptual framework for the Digital Reverse Logistics Twin, with a focus on human-centricity and sustainability in digital reverse logistics transformation.
• WP2 – Data-Driven Decision-Support System
  Development of an integrated decision-support system combining data analytics, optimization, and simulation to support human-centric and sustainable planning and management of reverse logistics systems.
• WP3 – Human-Centric Flexible Recycling Operations
  Development of a human-in-the-loop recycling process model to support safe, flexible, and efficient recycling processes.
• WP4 – Responsible Use of Generative AI
  Evaluation of the effective, transparent, and ethical use of generative AI and large language models in developing the digital reverse logistics twins and supporting sustainability-oriented logistics education. 
• WP5 – Project Management and Dissemination
  Coordination of project activities and different WPs and dissemination of results to academic, industrial, and societal stakeholders.