A hybrid particle swarm optimization algorithm for bi-criteria flexible job-shop scheduling problem
This paper presents a hybrid particle swarm optimization algorithm (HPSO) for solving the bi-criteria flexible job shop scheduling problem. Two minimization objectives- the maximum completion time (makespan) and the total workload of all machines are considered simultaneously. In this study, a novel discrete particle swarm optimization (PSO) algorithm was proposed, which incorporates well-designed crossover and mutation operators concurrently. Then, an external Parteo archive was developed to memory the Pareto optimal solutions found so far. In addition, to improve the efficiency of the scheduling algorithm, a speed-up method was devised to decide the domination status of a solution with the archive set. Experimental results on two well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the HPSO algorithm is superior to the existing present algorithms in term of both search quality and computational efficiency.
Flexible job shop scheduling problem Particle swarm optimization Multi-objective optimization Pareto archive set
Junqing Li Quanke Pan Shengxian Xie Jing Liang Liping Zheng Kaizhou Gao
School of Computer, Liaocheng University, Liaocheng, 252059 School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
1537-1541
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）