Disassembly Line Balancing for Sustainable Manufacturing: A Systematic Review of Models, Algorithms, and Future Directions

Jiaju Peng; Guangdong Tian; Duc Truong Pham; Hao Huang; Hongbin Chi; Songang Zhang

Abstract

Disassembly line balancing (DLB) has become an important technology for sustainable manufacturing, implementation of circular economy and recovery of end-of-life (EOL) products. With the increase in the complexity of waste electrical and electronic equipment, electric vehicle batteries and human-robot cooperative disassembly systems, DLB research has progressed from conventional deterministic optimization towards intelligent, uncertainty aware and sustainability oriented decision-making. In this paper, we present a detailed and thorough survey of recent advancements in DLB by analysing 135 representative papers published from 2003 to 2025, where about 80% were published after 2020. A Grounded Theory based literature review method was applied to systematically classify the existing research according to the disassembly representation methods, optimization objectives, mathematical models, uncertainty modelling strategies and solution algorithms. Special emphasis was put on the development of intelligent optimization techniques such as exact algorithms, heuristic methods, swarm intelligence, hybrid meta-heuristics, stochastic and fuzzy programming and recent reinforcement learning approaches. Additionally, this review suggests an integrated analytical framework which reveals the connections between optimization objectives, modelling paradigms and algorithmic developments, providing a better understanding of the technological progress of DLB. According to a comprehensive evaluation of the current achievements and the remaining shortcomings, six promising research fields are identified, including digital twin supported adaptive disassembly, intelligent decision-making assisted by large language models, multi-agent collaborative optimisation, human-robot cooperation, carbon aware circular manufacturing and data driven autonomous disassembly systems. This review not only summarises the present status of DLB research but also establishes a systematic guide for the development of next generation intelligent disassembly systems towards Industry 5.0 and sustainable manufacturing.

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