Industrial application of multi-robot destructive disassembly line balancing under component non-disassemblability for multi-product scenarios

Zeqiang Zhang, Lei Guo, Yu Zhang, Yanqing Zeng, Haolin Song, Yan Li, Haiye Chen and Muhammad Waqas Aslam

International Journal of Production Research, 2025, vol. 63, issue 18, 6801-6826

Abstract: With the continuous development of automation level, multi-robot collaborative disassembly is becoming a new trend in intelligent remanufacturing and industrial automation. However, current robotic disassembly frequently neglects the issue of component non-disassemblability (CND), which stems from the uncertain state of end-of-life products. To bridge this gap, this study integrates destructive disassembly into multi-robot and multi-product scenarios, addressing resource constraints and CND in a novel industrial context. A mixed-integer linear programming model is developed for the multi-robot destructive disassembly line balancing problem. The model incorporates component failure and hazard attributes, aiming to optimise cycle time, total energy consumption, peak energy consumption, and disassembly profit. To solve the model, an improved multi-objective water cycle algorithm is proposed, featuring four-layer encoding and a sequential crossover mechanism. Comparative evaluations demonstrate that the algorithm outperforms 12 multi-objective optimisation methods in four different disassembly test cases. Additionally, two engine disassembly case studies validate the model and algorithm. Results show that the proposed approach reduces cycle time by 13.8%, decreases total energy consumption by 10.9%, and enhances disassembly profitability by 3.1% compared to leading algorithms. The industrial case studies further demonstrate the method’s applicability, highlighting its potential for driving sustainability and efficiency in intelligent remanufacturing.

Date: 2025
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DOI: 10.1080/00207543.2025.2487923

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