Multi-objective optimization for U-shaped disassembly line balancing problem with stochastic operation times

To better reflect the uncertainty of actual disassembly task time, a multi-objective U-shaped mathematical model of disassembly line balancing problem was formulated by considering the stochastic operation times. A Pareto based hybrid artificial fish swarm algorithm was proposed for overcoming the deficiency that failure to balance each objective of traditional method in solving multi-objective problems. For reducing the repeated search in parallel foraging of fish swarm, a serial foraging way adopting the self-adaptive visual field was introduced, and the introduction of Pareto non-inferior solutions set realized the diversity of fish swarm optimization results. A simulated annealing operation was operated on the sequence results which could avoid the local optimum. In addition, the crowding distance was employed as an evaluation mechanism to filter and retain the elite solutions. Furthermore, the elite solutions were added to the next iteration of population which speeded up the convergence rate. The proposed algorithm was applied to one certain printer disassembly instance including 55 disassembly tasks. The effectiveness and superiority were validated through the comparison of proposed algorithm with basic artificial fish swarm algorithm and simulated annealing algorithm. © 2018, Editorial Department of CIMS. All right reserved.