Distributed Heterogeneous Co-Evolutionary Algorithm for Scheduling a Multistage Fine-Manufacturing System With Setup Constraints

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Guanghui Zhang;Bo Liu;Ling Wang;Keyi Xing
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引用次数: 0

Abstract

In the context of customization and personalization, the multistage fine-manufacturing system has become emerging in modern manufacturing industries. In this study, the scheduling problem of such a system called distributed hybrid differentiation flowshop scheduling problem is addressed for the first time to minimize makespan. The manufacturing process has three dedicated stages, including distributed fabrication for jobs, assembly from jobs to products, and further differentiation for products to meet customized requirements. Due to the scheduling complexity of the problem, the evolutionary algorithm is designed. First, the population is initialized heuristically and divided into three subpopulations with different identities based on the quality. The identity will be transited dynamically along with evolution. Second, a distributed heterogeneous global exploration strategy is designed to coevolve three subpopulations. According to identity differences, different subpopulations will choose their suitable learning operators and learning strengths to make full use of superior search knowledge. Third, to enhance search intensification, a problem-specific local exploitation strategy consisting of a variable neighborhood local search and a random block local search is devised and carried out adaptively. Fourth, by organizing the global exploration and local exploitation, a novel distributed heterogeneous co-evolutionary algorithm (DHCA) is proposed. The effect of parameter setting on DHCA is investigated by design-of-experiment and computational experiments are carried out to evaluate the algorithm. The results validate the effectiveness of special designs and show that DHCA are more effective and efficient than the compared state-of-the-art algorithms in solving the considered problem.
有设置限制的多级精细制造系统的分布式异构协同进化算法。
在定制化和个性化的背景下,多工序精细制造系统在现代制造业中崭露头角。在本研究中,首次解决了这种系统的调度问题,即分布式混合分化流水车间调度问题,以最小化生产周期。制造过程有三个专门阶段,包括作业的分布式制造、从作业到产品的装配,以及产品的进一步分化以满足定制要求。鉴于该问题的调度复杂性,设计了进化算法。首先,对种群进行启发式初始化,并根据质量将其分为三个具有不同身份的子种群。身份将随着进化而动态转换。其次,设计一种分布式异构全局探索策略来共同进化三个子种群。根据身份差异,不同子群将选择适合自己的学习算子和学习强度,以充分利用优势搜索知识。第三,为了提高搜索强度,设计了由可变邻域局部搜索和随机块局部搜索组成的特定问题局部开发策略,并自适应地执行。第四,通过组织全局探索和局部利用,提出了一种新型分布式异构协同进化算法(DHCA)。通过实验设计研究了参数设置对 DHCA 的影响,并对算法进行了计算实验评估。结果验证了特殊设计的有效性,并表明在解决所考虑的问题时,DHCA 比其他最先进的算法更有效、更高效。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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