摘要
考虑到现实流水车间调度中设备具有恶化特性,针对作业处理时间是其开始时间的线性递增函数的流水车间调度问题,建立了最小化最大完成时间和总延迟时间的多目标优化模型;进而设计了一种基于分解的自适应多种群多目标遗传算法进行求解.该算法将多目标优化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求解过程.在每次迭代时,根据种群在目标空间和解空间的分布情况,自适应地为当前求解的子问题分别构造子种群进行求解.通过对数值算例仿真实验,验证和分析了所提出的算法在解决该问题上能够获得较好质量和分布性的非支配解集.
Since the deteriorating effect of the facilities usually appears in the real-world flow shop scheduling, a multiobjective optimization model is built in order to minimize the makespan and the total tardiness simultaneously, where the normal processing time of job on each machine is a linear increasing function of its starting time. Then a decomposition based adaptive multipopulation multiobjective genetic algorithm is proposed to solve it. The proposed algorithm decomposes the multiobjective optimization problem into multiple single objective subproblems, which will be introduced to be solved step by step. According to the distribution of population in the objective space and solution space at each iteration, the current solved subproblems are solved by constructing the subpopulation, respectively. Experimental results on instances show that the proposed algorithm can get better quality and distributed nondominated solution set in solving the proposed problem.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2016年第11期2941-2950,共10页
Systems Engineering-Theory & Practice
基金
国家杰出青年科学基金(71325002
61225012)
国家自然科学基金(71071028
71001018)~~
关键词
流水线调度
设备恶化
多目标优化算法
多种群
分解方法
flow shop scheduling
machine deterioration
multiobjective optimization algorithm
multipopulation
decomposition approach
