知识驱动的模因算法求解分布式绿色柔性调度  

Knowledge-driven memetic algorithm for distributed green flexible job shop scheduling problem

作　　者：李瑞 王凌[2] 龚文引[1] LI Rui;WANG Ling;GONG Wenyin(School of Computer Science,China University of Geosciences,Wuhan 430074,China;Department of Automation,Tsinghua University,Beijing 100084,China)

机构地区：[1]中国地质大学(武汉)计算机学院,湖北武汉430074 [2]清华大学自动化系,北京100084

出　　处：《华中科技大学学报（自然科学版）》2022年第6期55-60,共6页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(62076225);湖北省杰出青年基金资助项目(2019CFA081)。

摘　　要：为求解以最小化最大完工时间和总能量消耗为目标的多目标分布式柔性作业车间调度问题,提出了混合多目标模因算法.设计了集成工序优先交叉、通用交叉和双点交换交叉的全局搜索算子,提出了基于析取图和临界块的变邻域搜索算子以加强收敛能力,针对总能量消耗的目标,提出了从半主动解码到主动解码再到全主动解码的节能策略.为了验证提出算法的有效性,设计了大量实验(包括田口正交实验、分离实验和对比实验),并对比了其他研究者最近提出的算法,实验结果表明提出的算法在分布式绿色柔性作业车间调度问题上效果优于对比算法.To solve the multi-objective distributed green flexible job shop scheduling problem(MDGFJSP) with minimizing makespan and total energy consumption,a hybrid multi-objective memetic algorithm(HMMA) was proposed. It contained three innovations: a global search operator was designed which integrated precedence operation crossover,universal crossover,and twopoint exchange crossover;a variable neighborhood search based on disjunctive graph and critical block was proposed to enhance convergence;aiming at total energy consumption,an energy-saving strategy was designed which transformed from semi-active schedule to active schedule to full-active schedule.To verify the effectiveness of the HMMA,many experiments including Taguchi orthogonal,ablation,and comparison experiments were executed. Moreover,a state-of-art algorithm was compared with the HMMA.The experiment results show that the HMMA is superior to the comparison algorithm for MDGFJSP.

关 键 词：混合多目标模因算法 知识驱动 多目标优化 全主动调度 分布式柔性作业车间调度问题 绿色调度 

分 类 号：TH165[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]