摘要
流水车间调度问题广泛存在于企业生产过程中,优化的调度方案可以提高企业生产效率,降低生产成本。提出了基于混沌量子粒子群优化算法并应用于求解置换流水车间调度问题,该算法在量子粒子群算法(QPSO)的基础上,引入了混沌机制,在保持QPSO算法收敛速度快的同时,利用混沌机制的遍历性,克服了QPSO易陷入局部极小值的缺点。同时提出了一种新的混沌变量到工件排序的编码方案,能够完整保留混沌的遍历性。仿真结果验证了所提出的新的调度算法能更好地探索更优解,同时不失去量子粒子群算法的收敛速度。
Flow-shop scheduling problem （FSP） widely exists in enterprise production processes. Optimal scheduling method can improve productivity and reduce production cost. In this paper, an optimization algorithm based on the chaotic quantum-behaved particle swarm is proposed to solve the permutation flow-shop scheduling problem, in which the chaotic mechanism is introduced into quantum- behaved particle swarm optimization （QPSO） such that the shortcoming of easily falling into local minimum for QPSO can be avoided. Meanwhile, the fast convergence speed of QPSO can be kept in this proposed algorithm. Besides, a new representation scheme is proposed to overcome the difficulties of conversion from chaotic variable into job sequence. The simulation results verify the effectiveness of the proposed algorithm in this work.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2013年第3期325-331,共7页
Journal of East China University of Science and Technology
基金
国家自然科学基金(61174040
61104178)
中央高校基本科研业务费专项基金
关键词
粒子群优化
量子粒子群优化
混沌优化
流水车间调度问题
particle swarm optimization
quantum-behaved particle swarm optimization
chaotic optimization
flow-shop scheduling problem
