Energy-efficient human-robot collaborative U-shaped disassembly line balancing problem considering turn on-off strategy: Uncertain modeling and solution method

Abstract

The quantity of waste automobile is becoming very large. Waste automobile not only occupies resources, but also easily pollutes the environment. How to realize the efficient and green treatment of recycled automobile is a hot topic in the industrial circular economy today. The disassembly line is the most efficient way to address large-scale waste automobile. Therefore, this paper takes the disassembly experiment of recycled automobile engine as the information orientation to construct energy-efficient human-robot collaborative U-shaped disassembly line balancing (HRU-DLB) framework considering turn on-off strategy. An engine disassembly information modeling method is proposed to address the issue on the actual disassembly space limitation. Establish a based-normal cloud HRU-DLBP mathematical model including disassembly smoothness, disassembly energy consumption (DEC), disassembly cost, disassembly idle time and disassembly carbon emission (DCE). To further reduce the disassembly energy consumption and carbon emission, the well-accepted energy-saving measure, known as the turn on-off strategy, is also integrated. Subsequently, a hybrid multi-objective optimization algorithm called ALNS-NSGA II, which combines the NSGA-II algorithm and adaptive large-scale neighborhood search algorithm is developed to explore the optimal Pareto solution set. Finally, the novel behavioral decision model is proposed to select the optimal HRU-DLB scheme. The comparative analysis shows that the turn on-off strategy can reduce DEC by 26 % and DCE by 3.1 % in a cycle time, respectively. The computational results confirm the feasibility and effectiveness of the proposed ALNS-NSGA II in solving the HRU-DLBP. The comparative analysis and sensitivity analysis demonstrate that the proposed behavioral decision model has better ranking and classification effects.