缓冲管理在解决汽车工业中一个真正的排序问题——油漆车间4.0概念

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS Archives of Control Sciences Pub Date : 2023-07-20 DOI:10.24425/ACS.2019.130203
Sara Bysko, J. Krystek, Szymon Bysko, R. Lenort
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引用次数: 5

摘要

本文提出了一个在文献中被广泛考虑的汽车排序问题。与实际汽车生产中的问题相比,研究人员所考虑的问题只是一个简化的问题。因此,一个新的概念,称为油漆车间4.0。,从排序问题的角度来考虑。本文是先前进行的研究的一部分,确定为带缓冲器的汽车排序问题(CSPwB),将原始问题扩展到配备缓冲器的生产线问题。新的创新方法是基于工业4.0和缓冲管理系统的理念。本文讨论了迄今为止已经发展起来的排序算法。提出了原始的后续排序算法,该算法仍在作者的开发中。研究的主要目标是找到最有效的算法,以最小化喷漆枪的更换,并将必要的颜色更换与周期性的喷漆枪清洗同步。研究表明,本文提出的最先进的算法优于其他已测试的方法,因此在汽车工业中具有广阔的应用前景。
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Buffer management in solving a real sequencing problem in the automotive industry – Paint Shop 4.0 concept
The paper presents a Car Sequencing Problem, widely considered in the literature. The issue considered by the researchers is only a reduced problem in comparison with the problem in real automotive production. Consequently, a new concept, called Paint Shop 4.0., is considered from the viewpoint of a sequencing problem. The paper is a part of the previously conducted research, identified as Car Sequencing Problem with Buffers (CSPwB), which extended the original problem to a problem in a production line equipped with buffers. The new innovative approach is based on the ideas of Industry 4.0 and the buffer management system. In the paper, sequencing algorithms that have been developed so far are discussed. The original Follow-up Sequencing Algorithm is presented, which is still developed by the authors. The main goal of the research is to find the most effective algorithm in terms of minimization of painting gun changeovers and synchronization necessary color changes with periodic gun cleanings. Carried out research shows that the most advanced algorithm proposed by the authors outperforms other tested methods, so it is promising to be used in the automotive industry.
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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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