摘要
为高效地求解带调整时间的多目标流水车间调度问题,提出了一种多目标混合遗传算法,此算法依据基于Pareto优于关系的个体排序数和密度值计算适应度,保持解的多样性,并采用非劣解并行局部搜索策略,提高算法的搜索效率。此外,引入精英策略保证算法的收敛性,在进化过程中通过淘汰掉个别最差个体,进一步加快解的收敛速度。仿真结果表明,新算法能够有效地解决带调整时间的多目标流水车间调度问题。
To efficiently solve multi-objective flow shop scheduling problem with setup times, a new multi-objective hybrid genetic algorithm （MOHGA） was proposed. A Pareto parallel local search strategy was used. The individual fitness based on the rank of the individual and its density value was evaluated. An elitist strategy was adopted to improve the convergence of the algorithm and preserve diversity in the population. The concept of Pareto dominance was used to assign fitness to the solutions and in the local search procedure. The simulation results show that the proposed algorithm could solve multi-objective flow shop scheduling problem with setup times effectively.
出处
《工业工程与管理》
2008年第5期1-5,共5页
Industrial Engineering and Management
基金
国家自然科学基金资助项目(70572098)
关键词
调整时间
多目标优化
流水车间调度
混合遗传算法
局部搜索
setup times
multi-objective optimization
flow shop scheduling~ hybrid geneticalgorithm
local search
