https://orcid.org/0000-0002-2403-8084
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ABSTRACT
The growing diversity and volume of e-waste products are emerging as significant and complex challenges from environmental, economic, and recycling perspectives. This surge not only hampers the effective management and conservation of precious resources and strategically important materials (SIMs) but also intensifies the problem. Addressing this pressing global issue necessitates a holistic and intelligent decision-making model capable of effortlessly handling the vast variety and volume of e-waste products. This paper introduces a novel multi-criteria decision support system (DSS) that combines AI visual recognition with a multi-fuzzy model to precisely determine the optimal End-of-Life (EoL) recovery route. Additionally, the functionality of the proposed DSS is showcased through a pilot implementation and case study examples, emphasising the advantages of a fully automated, reliable, and efficient EoL management for e-waste.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The dataset and trained CNN network used in this AI-based study are available on GitHub at [https://github.com/Sams5879/VEIDD_system.git]. For further supporting data related to the decision support system, please contact the corresponding author, Ehsan Simaei, for access under confidentiality agreements to allow for commercialisation.
Additional information
Notes on contributors
Ehsan Simaei
Ehsan Simaei M. E, PhD, PGDip, Research Associate in Intelligent Automation and Robotics. Expertise: Robotics for Sustainable, Space Robotics, Lab Automation.
Shahin Rahimifard
Shahin Rahimifard BSc, MSc, PhD, CEng, FIMechE, FHEA Professor of Sustainable Engineering. Expertise: Sustainable Manufacturing, Sustainable Design, Automation in Remanufacturing/Reuse/Recycling, Business Models.