摘要
针对ET指标的批量流水线调度问题,提出了差分进化调度算法。该算法采用基于实数的编码方式,利用最优目标个体的扰动产生变异个体,通过变异个体与目标个体的交叉产生试验个体,提高了最优目标个体信息共享,并结合模拟退火算法给出了两种混合求解策略。仿真试验表明了所得算法的可行性和高效性。
A Differential Evolution（DE） scheduling algorithm is presented for solving the Lot-streaming Flow Shop Scheduling Problem（LFSP） with the objective of minimizing the total weighted earliness and tardiness.In the proposed algorithm,the Most Position Value（MPV） rule is applied to enable the continuous DE algorithm to be used in all kinds of sequencing problems,mutant individual is constructed by the optimal target individual,and trial individual is obtained through crossover of target and mutant individual.Then Simulated Annealing（SA） algorithm is presented to enhance the local searching ability. Finally,two hybrid algorithms are developed by combining the proposed DE and SA algorithms.The computational results show that the hybrid differential evolution algorithms presented is effective and efficient for the LFSP.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第21期47-50,93,共5页
Computer Engineering and Applications
基金
国家自然科学基金No.60874075
No.70871065
No.60774082
No.60834004
中国博士后科学基金项目No.20070410791
数字制造装备与技术国家重点实验室开放课题(华中科技大学)~~
关键词
批量流水线调度
ET指标
差分进化算法
模拟退火算法
混合算法
lot-streaming flow shop scheduling
weighted earliness and tardiness
differential evolution algorithm
simulated annealing algorithm
hybrid algorithm
