A Decision-Support Framework for Smart and Sustainable Reverse Logistics Network Design

Abstract

The Fourth Industrial Revolution, namely Industry 4.0, has provided opportunities for digitalization and paradigm shifts in many industries and business sectors. Reverse logistics is currently being increasingly focused on by worldwide companies and governments due to the pressure on sustainable development and circular economy. Through the gradual but steady adoption of several disruptive technologies in Industry 4.0, the traditional reverse logistics operations will be dramatically improved with the increasing use of the internet of things (IoT), cyber-physical systems (CPS), artificial intelligence (AI), digital twin, smart robots and machines, etc., which may eventually lead to a smart and sustainable transformation of Reverse Logistics 4.0.

However, there is a lack of a systematic analysis of the impacts of these Industry 4.0 technologies on reverse logistics. Moreover, the adoption of new technologies will further complicate the reverse logistics network design at the initial stage, which involves many stakeholders with often contradictive objectives. To fill these gaps, this Ph.D. project first presents a comprehensive literature review and conceptualization of Reverse Logistics 4.0 in order to provide a holistic and systematic analysis of the implications of disruptive technologies and Industry 4.0 for smart and sustainable reverse logistics transformation. Based on the conceptualization, an improved two-level decision-support framework, which combines both multi-objective optimization and dynamic simulation, is proposed to better help with robust strategic decisions under high dynamicity and uncertainty.

The methodological integration leads to the development of a conceptual framework for the digital reverse logistics twin. It represents a high level of methodological and system integration that can potentially connect the physical system and data with various analytical models for both proactive and real-time decision supports in reverse logistics management. Finally, this Ph.D. project presents several managerial implications and research implications for both industrial practitioners and academic researchers.