A constraint programming-based lexicographic-Pareto approach for balancing two-sided robotic disassembly lines with 7-axis robots

Robotic disassembly lines play a pivotal role in remanufacturing by enabling automated operations. In two-sided disassembly scenarios involving large-scale products such as automobiles, their high load capacity significantly reduces the labor intensity of manual disassembly and eliminates the need for lifting equipment, thereby streamlining the process flow. When equipped with mobility systems, 7-axis robots can flexibly switch between multiple workstations, facilitating both rapid adaptation to process changes and precise execution of spatially heterogeneous disassembly tasks. However, despite these advantages, systematic research on the integration of mobile disassembly robots within disassembly line applications remains limited. To address this gap, this study integrates 7-axis mobile robots into two-sided disassembly lines and models the system using both mixed-integer programming and constraint programming approaches. The proposed models aim to minimize construction costs and ensure balanced workload distribution across stations. A novel constraint programming-based lexicographic-Pareto approach is developed to solve the resulting multi-objective optimization problem, this method is capable of generating verified Pareto frontiers for small-scale instances and providing high-quality approximate Pareto solution sets for large-scale problems. In the numerical experiments, a sensitivity analysis of key algorithm parameters is first conducted to achieve a balance between computational efficiency and solution quality. Subsequently, the proposed method is benchmarked against nine existing algorithms across twenty datasets to validate its effectiveness. Its practical feasibility is further demonstrated through an application to the disassembly of drum washing machines. The results show that, compared to conventional fixed-robot disassembly lines without cross-station coordination, the mobile robot configuration achieves a 10.7% reduction in total cost and a 66.7% improvement in robot workload balance, offering a promising pathway for advancing remanufacturing practices.