摘要
当今环境问题日益严峻,绿色制造备受关注.同时,全球化的发展令分布式制造模式日趋普遍.分布式绿色调度研究旨在推进企业在分布式制造模式下经济指标和绿色指标的协同优化.由于生产过程中工件在不同机器间的运输时间难以忽略,因此本文研究考虑运输时间的分布式绿色柔性作业车间调度问题,以最小化最大完工时间和加工过程总能耗为优化目标,提出一种协同群智能算法.首先,将问题分解为工序排序和机器分配两个子问题,设计多种针对两子问题的搜索操作;其次,采用多种规则协同初始化种群;进而,对种群进行分层协同优化,即基于信息交互的工序排序搜索和基于Q-learning的机器分配搜索,并采用基于分解的解替换原则平衡种群的收敛性和多样性;最后,对精英解进行局部增强搜索,并对所得非支配解执行能耗调整.通过基于不同数据集的仿真实验,验证了算法各环节的有效性,同时与现有算法的对比结果也表明了所提算法能够更有效求解考虑运输时间的分布式绿色柔性作业车间调度问题.
In the face of current serious environmental issues, green manufacturing is attracting much attention. In addition, distributed manufacturing has become increasingly common with the development of globalization. Given a distributed manufacturing environment, we study the distributed green scheduling problem to simultaneously optimize both economic objectives and green objectives. During practical manufacturing processes, the transportation times of jobs between different machines cannot be ignored.Accordingly, this paper proposes a cooperative memetic algorithm to solve distributed green flexible job shop scheduling considering the transportation time(DGFJSPT) to minimize the makespan and total energy consumption. First, the problem is decomposed into two sub-problems, i.e., operation sequence and machine assignment. Second, several problem-specific search operators are designed.Third, a hierarchical cooperative optimization scheme is designed for the two sub-problems, including an information interactionbased search for the operation sequence and a Q-learning-based search for the machine assignment. Then, a solution selection strategy based on decomposition is used to balance the convergence and diversity. In addition, local intensification and an energy-saving strategy are used to further improve the performance. Numerical experiments are then carried out based on different benchmarking sets, and the results demonstrate the effectiveness of the specific designs. The comparisons indicate that the proposed algorithm is more effective than existing algorithms in solving the DGFJSPT problem.
作者
王凌
王晶晶
WANG Ling;WANG JingJing(Department of Automation,Tsinghua University,Beijing 100084,China)
出处
《中国科学：技术科学》
EI
CSCD
北大核心
2023年第2期243-257,共15页
Scientia Sinica(Technologica)
基金
国家自然科学基金(批准号:61873328)
国家杰出青年科学基金(编号:61525304)资助项目。
关键词
分布式制造
绿色调度
柔性作业车间调度
协同群智能优化
distributed manufacturing
green scheduling
flexible job shop scheduling
cooperative memetic algorithm
