Techno-economic and environmental benefits-oriented human–robot collaborative disassembly line balancing optimization in remanufacturing

The remanufacturing process, driven by human–robot collaboration technology, is becoming an important carrier for the circular economy, contributing to economic development while significantly reducing environmental pressure. However, existing researches on human–robot collaboration disassembly line have certain limitations and fails to address economic and environmental considerations adequately. Therefore, in this study, a techno-economic and environmental benefit-oriented human–robot collaborative disassembly line balancing problem (TEBHRC-DLBP) was formulated that requires the utilization of human and robot resources to improve disassembly efficiency and quality and to facilitate decision-making on recycling options for disassembled parts to maximize economic and environmental benefits. First, a mixed-integer programming (MIP) model was developed for the TEBHRC-DLBP to minimize the number of workstations, minimize the smoothing index, and maximize techno-economic and environmental benefits. Second, a multi-objective immune genetic algorithm (MIGA) was developed to solve the proposed TEBHRC-DLBP efficiently. A five-layer encoding method and a triple decoding strategy were constructed based on the problem characteristics, and immune operators were introduced into the well-known NSGA-II structure to interfere with the global search process with a certain degree of strength to avoid invalid work and to improve algorithm efficiency. In addition, the correctness and validity of the proposed MIP model and the MIGA were verified using the case of a small-scale personal computer. In 21 benchmark tests with scales ranging from 7 to 148, the proposed MIGA achieved significantly better results than the seven algorithms reported in the literature and obtained the best value for 16 benchmarks. Finally, the application of the MIGA to the disassembly case of a power battery module demonstrates its good stability, convergence, and diversity, as well as its excellent practical application ability. In particular, the proposed method can change the disassembly scheme according to different disassembly planning periods to maximize economic and environmental benefits.