A human-centric order release method based on workload control in high-variety make-to-order shops towards Industry 5.0

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Lin Ma, Ray Y. Zhong, Mingze Yuan, Kai Ding, Matthias Thürer, Yanghua Pan, Ting Qu, Geroge Q. Huang
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引用次数: 0

Abstract

Industry 5.0 emphasizes a human-centric concept, aiming to construct highly intelligent, sustainable, and resilient manufacturing systems. While a large body of literature has explored its concepts, architectures, enabling technologies, and practical applications, literature specifically focused on production planning and control solutions in industry 5.0 shops are scarce. Recent literature indicates that the well-being and skills of human workers significantly impact shop performance due to their highly variable activities and behaviors. Workload control has been recognized as a simple yet effective solution to mitigate the effects of high variability - both human and machine - through a three-layer filter for high-variety make-to-order shops, offering potential for Industry 5.0. However, the existing workload control concept has two significant limitations. First, it primarily focuses on the workload of machines while ignoring the potential impacts of humans, and; Second, this concept relied on the fixed processing times and lack flexibility to cope with changes in human subjective behaviors. In response, this study first presents a human-centric order release method based on workload control, enhancing its adaptability by considering uncertain human processing times. Furthermore, we introduce five shop floor priority dispatching rules to further investigate the potential impacts of additional factors on our proposed method. Simulation results show that the human-centric method outperforms the traditional machine-centric method, particularly in pure job shops. Meanwhile, when combining the human-centric order release method with the shop floor dispatching rules, the load-oriented dispatching rules significantly improve the shop's performance in terms of throughput time, while the time-oriented dispatching rules increase order delivery performance. Counterintuitively, integrating human-centric concept into the shop floor dispatching stage is noteworthy, i.e. human-centric shop floor dispatching rule. It does not enhance shop performance compared to the original dispatching rules, but rather deteriorates the performance of order release on most measures. The findings of this study have important implications for both research and practice in Industry 5.0.
面向工业5.0的基于工作量控制的以人为中心的订单释放方法
工业5.0强调以人为中心的概念,旨在构建高度智能、可持续和有弹性的制造系统。虽然大量的文献研究了工业5.0的概念、体系结构、支持技术和实际应用,但是专门关注工业5.0车间中的生产计划和控制解决方案的文献很少。最近的文献表明,由于人类工人的活动和行为高度可变,他们的幸福感和技能显著影响商店绩效。工作量控制已经被认为是一种简单而有效的解决方案,可以通过三层过滤器来减轻高可变性(人和机器)的影响,为多种定制商店提供了潜在的工业5.0。然而,现有的工作负载控制概念有两个明显的局限性。首先,它主要关注机器的工作量,而忽略了人类的潜在影响;其次,这一概念依赖于固定的处理时间,缺乏灵活性来应对人类主观行为的变化。为此,本研究首先提出了一种基于工作量控制的以人为中心的订单释放方法,并考虑了人工处理时间的不确定性,增强了该方法的适应性。此外,我们引入了五个车间优先调度规则,以进一步研究其他因素对我们提出的方法的潜在影响。仿真结果表明,以人为中心的方法优于传统的以机器为中心的方法,特别是在纯作业车间中。同时,当以人为中心的订单释放方法与车间调度规则相结合时,以负荷为导向的调度规则在生产时间上显著提高了车间的绩效,而以时间为导向的调度规则提高了订单交付绩效。与直觉相反,将以人为中心的概念融入到车间调度阶段是值得注意的,即以人为中心的车间调度规则。与原有的调度规则相比,它并没有提高车间绩效,而是在大多数指标上降低了订单释放的绩效。本研究结果对工业5.0的研究和实践具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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