Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure

摘要

A disassembly line balancing problem (DLBP) exists in the recycling process of end-of-life (EOL) products. It involves such factors as uncertainty of disassembly time and disassembly failure risk. Effective decisions can be made by taking them into full consideration. Under the constraints of disassembly precedence relationships and cycle time, this work establishes a stochastic multi-objective DLBP model subject to disassembly failure based on a disassembly AND/OR graph of EOL products. It considers disassembly failure risk and comprehensively evaluates the profit, energy consumption, average idle time of workstations, and hazard disassembly. Then, a new multi-objective discrete brainstorming optimizer that combines stochastic simulation is proposed for obtaining high- quality feasible solutions. Experimental results show the validity of the proposed algorithm. It outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimizer.