Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.