Trigonometric-based mechanisms hybridized African vulture optimization algorithm for multi-manned disassembly line balancing involving worker heterogeneity and collaboration

The rapid replacement of large-scale end-of-life (EOL) heavy machineries like automobiles, aircrafts and industrial robots necessitates efficient resource recovery to promote sustainable and eco-friendly manufacturing. This study therefore focuses on multi-manned disassembly lines in recycling large-scale products, bridging the gap between theory and practice. We introduce complex, safety-sensitive tasks that require collaborative efforts of multiple workers in the Multi-Manned Disassembly Line Balancing Problem (MMDLBP) for the first time. We also consider worker heterogeneity due to varying training and skills, as manual stations are inherently worker-dependent in nature. To address this Multi-Manned Disassembly Line Balancing Problem with Worker Heterogeneity and Collaboration (MMDLBP-HC), we establish a mixed-integer programming model to minimize cycle time and labor cost simultaneously. Given its NP-hard nature, we develop a Multi-Mechanism-Enhanced Bi-Objective African Vultures Optimization Algorithm (MBAVOA). It employs specified encoding with numerical branching, precedence-priority concurrent decoding, and selective opposition-based learning. We also combine trigonometric-based mechanisms with the African vulture optimization algorithm (AVOA) to enhance exploration. Additionally, adaptive neighborhood search mechanisms are tailored for inter-individual information exchange. Numerical experiments compare MBAVOA to four meta-heuristics and an exact algorithm. The results demonstrate the model accuracy and the effectiveness of the encoding and decoding mechanisms, while MBAVOA outperforms benchmark algorithms significantly. Finally, we offer managerial applications to guide practitioners in balancing plan formation and training program design.