Analysis of production cycle-time distribution with a big-data approach

Tan, Xu; Xing, Lining; Cai, Zixing; Wang, Gai-Ge

doi:10.1007/s10845-020-01544-7

Cited by 15 publications

(7 citation statements)

References 26 publications

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“…In the textile industry distinctive works show that multicore CPU/GPU architectures and parallel computing are mainly employed for tasks like (virtual try-on) cloth simulation, clothcloth collision detection (Tang et al, 2013;Vassilev, 2016), cloth animation (Xue et al, 2017;Zang et al, 2018), authoring and simulating of cloth or garment patterns (Leaf et al, 2018); see (Figure 12). In the manufacturing sector, parallel algorithms are employed for tasks like dynamic production planning and cycle time forecasting (Nakamura et al, 2017;Tan et al, 2020;, or evaluation and ranking of alternative technologies on the basis of economic and technological criteria (Solgi et al, 2019). The main focus, though, of current research lies on the evolving technology of additive manufacturing.…”

Section: Collaborative Robots Complex Robotic Cells Interconnection and Safety Issuesmentioning

confidence: 99%

Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

Jimeno-Morenilla

Azariadis

Molina-Carmona

et al. 2021

Computers in Industry

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The traditional manufacturing sectors (footwear, textiles and clothing, furniture and toys, among others) are based on small and medium enterprises with limited capacity on investing in modern production technologies. Although these sectors rely heavily on product customization and short manufacturing cycles, they are still not able to take full advantage of the fourth industrial revolution. Industry 4.0 surfaced to address the current challenges of shorter product life-cycles, highly customized products and stiff global competition. The new manufacturing paradigm supports the development of modular factory structures within a computerized Internet of Things environment. With Industry 4.0, rigid planning and production processes can be revolutionized. However, the computerization of manufacturing has a high degree of complexity and its implementation tends to be expensive, which goes against the reality of SMEs that power the traditional sectors. This paper reviews the main scientific-technological advances that have been developed in recent years in traditional sectors with the aim of facilitating the transition to the new industry standard.

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Section: Collaborative Robots Complex Robotic Cells Interconnection and Safety Issuesmentioning

confidence: 99%

Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

Jimeno-Morenilla

Azariadis

Molina-Carmona

et al. 2021

Computers in Industry

View full text Add to dashboard Cite

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“…The operation speeds of various processes in a distributed system are different and unknown in advance, and the delivery of sent messages may take an unpredictable period of time. In most cases, a distributed system contains several processors that are interconnected by means of communication (Tan et al, 2020).…”

Section: Research Questionsmentioning

confidence: 99%

Big Data: Legal Regulation And Role In The Digitalization Of The Economy

Gubaydullina¹

2021

Global Challenges and Prospects of the Modern Economic Development Proceedings of Global Challenges and Prospects of the Modern

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“…Chen [10] classified existing approaches for forecasting the cycle time of a job into six categories: statistical analysis [10,32,43,54], simulation [34,43], artificial neural networks (ANNs, [6,7,33], case-based reasoning (CBR, [5], fuzzy theory [6,7], and hybrid approaches [6,19]. Recently, advanced data analysis techniques, such as big-data analysis and deep learning, have also been used for forecasting the cycle time of a job [47,[53][54][55]. However, the cycle time of a job is highly uncertain [49] and unpredictable even with these advanced data analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy dynamic-prioritization agent-based system for forecasting job cycle time in a wafer fabrication plant

Chen

Wang

2021

Complex Intell. Syst.

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A fuzzy dynamic-prioritization agent-based system was developed in this study to improve the forecasting of the cycle time of a job in a wafer fabrication plant (wafer fab). In this system, multiple fuzzy agents forecast the cycle time of a job from various viewpoints, after which the aggregation and evaluation agent aggregates these fuzzy cycle time forecasts using an innovative operator (i.e., the fuzzy weighted intersection) into a single representative value. Subsequently, the optimization agent varies the authority levels of the fuzzy cycle time forecasting agents to optimize the forecasting performance. A practical example was used to evaluate the effectiveness of the fuzzy dynamic-prioritization agent-based system. The experiment results indicated that the fuzzy dynamic-prioritization agent-based system outperformed three rival methods in improving forecasting accuracy. In addition, the forecasting performance could be enhanced by discriminating the authority levels of the fuzzy cycle time forecasting agents.

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Analysis of production cycle-time distribution with a big-data approach

Cited by 15 publications

References 26 publications

Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

Big Data: Legal Regulation And Role In The Digitalization Of The Economy

Fuzzy dynamic-prioritization agent-based system for forecasting job cycle time in a wafer fabrication plant

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