Improved Carnivorous Plant Algorithm for Human–Robot Collaborative U-Shaped Disassembly Line Balancing With Mobile Workers

The advancement of human–robot collaboration technology has positioned remanufacturing as a crucial part of the circular economy, driving both economic growth and environmental sustainability. In the era of Industry 5.0, these technologies enhance the efficiency and flexibility of disassembly tasks. However, most research on human–robot collaborative disassembly (HRCD) line balancing overlooks the mobility of workers. This study introduces a profit-oriented HRCD model incorporating mobile workers. To address large-scale HRCD challenges, it proposes a dynamic attraction rate mechanism that improves the traditional carnivorous plant algorithm (CPA), tackling issues of slow convergence and local optimization. The experimental framework includes three validation phases: 1) comparison with the exact solver IBM ILOG CPLEX Optimization Studio (CPLEX); 2) parameter sensitivity analysis; and 3) benchmarking against seven state-of-the-art algorithms. Results demonstrate that HRCD with mobile workers significantly boosts disassembly efficiency and reduces disassembly time compared to traditional methods. Additionally, it increases profits through flexible task allocation. In cases of incomplete disassembly, HRCD with mobile workers yields an average benefit increase of 87.64% over conventional disassembly modes. A comparative evaluation with other swarm intelligence algorithms further highlights the superior solution quality and time efficiency of the improved CPA.