| Summary: | In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of UiT The Arctic University of Norway’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to to learn how to obtain a License from RightsLink. 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 ...
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