摘要
针对供应链网络优化领域中的混合流水作业调度问题提出了一种新的多目标演化优化算法。给出了这类问题的通用优化模型,在此基础上,提出了基于流程的矩阵基因编码方案,动态适应度分配机制,并引入小生境保优策略构造了算法过程,利用收敛进程参数分析了算法的收敛性能。性能分析和算例实验表明算法对于高维多目标优化问题是有效的,且能够以较快的速度收敛。
A new evolutionary algorithm for solving multi-objective hybrid flow shop scheduling problem （HFSP） which is an important topic in supply chain network optimization is presented. The general model for the HFSP is proposed, and a matrix gene encoding method and a sort of fitness assignment strategy which can approach the optimum solutions with dynamic weighting are discussed. The algorithm process is presented by using elitist strategy. The convergent performance of the algorithm is analyzed by computing the progress measurement. The performance analysis and the experimental results show that the algorithm is effective for high-dimensional multi-objective problems and can converge to satisfactory solutions at a high speed.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2006年第3期327-331,共5页
Journal of Nanjing University of Science and Technology
基金
国家"863"计划(2003AA41302
2003AA4Z3370)
关键词
混合流水作业调度
多目标优化
演化计算
适应度分配机制
hybrid flow shop scheduling
multi-objective optimization
evolutionary computing
fitness assignment
