摘要
针对制造型企业普遍存在的流水车间调度问题,建立了以最小化最迟完成时间和总延迟时间为目标的多目标调度模型,并提出一种基于分解方法的多种群多目标遗传算法进行求解.该算法将多目标流水车间调度问题分解为多个单目标子问题,并分阶段地将这些子问题引入到算法迭代过程进行求解.算法在每次迭代时,依据种群的分布情况选择各子问题的最好解及与其相似的个体分别为当前求解的子问题构造子种群,通过多种群的进化完成对多个子问题最优解的并行搜索.通过对标准测试算例进行仿真实验,结果表明所提出的算法在求解该问题上能够获得较好的非支配解集.
Since the permutation flow shop scheduling problem exits extensively in manufacturing enterprises, a multiobjective flow shop scheduling problem with the objectives of minimizing the makespan and the total tardiness is investigated in this paper. In order to solve it, a multipopulation multiobjective genetic algorithm based on decomposition is proposed. The proposed algorithm decomposes the investigated problem into multiple single objective subproblems introduced into the iteration course step by step. At each iteration, multiple subpopulations are constructed for the current solved subproblems based on the distribution of population, which realizes the goal of solving them simultaneously. The evolution of multiple subpopulations can be used to search the optimal solutions of multiple subproblems. Experimental results on some instances show that the proposed algorithm can get better performance in solving the multiobjective permutation flow shop scheduling problem. © 2016, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2016年第10期1281-1288,共8页
Control Theory & Applications
基金
国家杰出青年科学基金项目(71325002,61225012)
国家自然科学基金项目(71671032,61673228)
流程工业综合自动化国家重点实验室基础科研业务费(2013ZCX11)~~
关键词
多种群
遗传算法
多目标优化
流水车间调度
Genetic algorithms
Iterative methods
Machine shop practice
Multiobjective optimization
Optimization
Scheduling
