Pareto Hybrid Ant Colony and Genetic Algorithm for Multi-Objective U-Shaped Disassembly Line Balancing Problem

Owing to the incapability of traditional methods in solving multi-objective U-shaped disassembly line balancing problem (UDLBP), a multi-objective ant colony genetic algorithm based on Pareto set is proposed to solve the UDLBP. In constructing the initial solution phase, the maximum operation time and the minimum disassembly cost difference were collaboratively considered as the heuristic information of the ants. The feasible disassembly sequence was searched using the ant colony algorithm, and the Pareto solution set was obtained based on the dominance relationship among the multiple objectives. Further, the Pareto non-inferior solutions of the ant colony algorithm were used as the chromosomes of the genetic operator. Moreover, the results of the genetic operator were fed back to the accumulation of pheromone on the optimal disassembly sequence. The crowding distance was regarded as the global pheromone update strategy to balance the effect of multi-objective function regarding the pheromone and thereby make the algorithm obtain better solutions readily. The proposed algorithm was applied to 52 disassembly task examples and a printer disassembly line instance. Compared with the Pareto ant colony algorithm, the performances of the three evaluation indicators of the 8 non-inferior solutions obtained using the proposed algorithm are improved by 50.43%, 3.25%, and 14.10%, respectively. In the example application, the proposed algorithm obtained eight balancing schemes, which verifies the effectiveness, superiority, and practicability of the proposed algorithm.