This study explores the impact of artificial intelligence (AI) in reverse logistics (RL) practices through a systematic literature review (SLR) comprising both bibliometric and content analyses. RL aims to optimize supply chain operations and environmental protection by maximizing product recovery, reducing waste, and saving materials and natural resources. Through a comprehensive SLR of 335 relevant studies published between 2015 and 2024, our paper categorizes AI technologies into three groups: evolutionary algorithms/metaheuristics, machine learning/deep learning, and fuzzy logic and methods. The content analysis further summarizes the application of these technologies in supporting forecasting, decision-making under uncertainty, and managing transportation, inventory, and operations in RL practices, including collection, recycling, reuse, repair, remanufacturing, refurbishing, cannibalization, and disposal. Furthermore, our findings suggest that future research should focus on increased use of machine learning and deep learning methods in RL practices, addressing uncertainty and stochasticity using AI methods, better understanding and analysis of consumer return behaviors with AI, AI-supported green and socially responsible RL practices, and the impact as well as the effective and responsible utilization of Generative AI (GenAI) and Large Language Models (LLMs) in human-AI and human–machine collaborative RL operations.
Dehabadi et al. (Wed,) studied this question.
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