Ant colony and particle swarm optimization algorithm-based solution to multi-objective flexible job-shop scheduling problems
基于蚁群粒子群算法求解多目标柔性调度问题

A hybrid of ant colony and particle swarm optimization algorithms was proposed to solve the multi-objective flexible job-shop scheduling problem based on the analysis of objectives and their relationship. The hybrid was formulated in a form of hierarchical structure. The ant colony algorithm was performed at the master level to minimize the total load and bottleneck load through selecting job-processing route, while the particle swarm optimization algorithm was carried out at the slave level to minimize the makespan through scheduling the operations with machines without violating the result from the master level. The transfer probabilities of ant between machines were designed by using heuristic information of processing time and machine load. The decoding method of particle vector was well designed in order to sequence operations of every machine based on the size relations of element priority values. The simulation and results from comparison with other algorithms demonstrate the effectiveness of the proposed algorithm.