Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing

Zhang, Jianhua; Qin, Wei; Wu, Lihui; Zhai, W.

doi:10.1016/j.compind.2014.06.002

Cited by 34 publications

(13 citation statements)

References 23 publications

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“…[32] proposed a Hopfield Neural Network (HNN) algorithm to solve the shop scheduling problem. [33] studied the rescheduling problem of semiconductor manufacturing systems and proposed a fuzzy neural network model. [34] designed a neural network model that predicts the impact of knowledge management on manufacturing performance.…”

Section: Literature Review In the Field Of Manufacturing Managementmentioning

confidence: 99%

Application of Data Mining in the Field of Human Resource Management: A Review

Ma¹,

Zhang²,

Song³

et al. 2019

Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)

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Data mining methods play an important role in customer management, manufacturing management, and financial management. This paper focuses on the latest research progress from 2012 to the present. The analysis of the progress of existing research will help researchers to better engage in research work in the future. The literature review is classified according to the research field and the data mining methods involved. In each of the research areas, we conduct research classification based on the data mining tools used. Through literature analysis, it can be seen that the degree of attention of neural networks has increased continuously in recent years.

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Section: Literature Review In the Field Of Manufacturing Managementmentioning

confidence: 99%

Application of Data Mining in the Field of Human Resource Management: A Review

Ma¹,

Zhang²,

Song³

et al. 2019

Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)

View full text Add to dashboard Cite

show abstract

“…Data transformation is the process of converting data from one format or structure into another. The pervasive method is normalization for the selected articles but few were conducted based on variance scaling [35,63], text mining [42,123], Fisher Z-transformation [44,47], binary vector transformation [91,93,117], Box-Cox transformation [84], and numerical into binary [85].…”

Section: Data Handlingmentioning

confidence: 99%

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Kim

Zheng

et al. 2018

Applied Sciences

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Data mining (DM) with Big Data has been widely used in the lifecycle of electronic products that range from the design and production stages to the service stage. A comprehensive analysis of DM with Big Data and a review of its application in the stages of its lifecycle will not only benefit researchers to develop strong research themes and identify gaps in the field but also help practitioners for DM application system development. In this paper, a brief clarification of DM-related topics is presented first. A flowchart of DM and the main content of the flowchart steps are given in which commonly used data preparation and preprocessing approaches, DM functions and techniques, and performances indicators are summarized. Then, a comprehensive review covering 105 articles from 2007 to 2017 on DM or Big Data applications in the electronics industry is provided according to the flowchart from various points of view such as data handling, applications of DM, or Big Data at different lifecycle stages, and the software used in the applications. On this basis, a diagram of data content for different knowledge areas and a framework for DM and Big Data applications in the electronics industry are established. Finally, conclusions and future research directions are given.

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“…Reallocation is an effective reaction to uncertainties and changes in the environments, and it has an important role in reducing the wasted time during an operation and increasing operation profitability (Zhang et al, 2014). Presenting strategies for allocation is one of the approaches to improve flexibility against disorder in natural disaster environments.…”

Section: Introductionmentioning

confidence: 99%

USAR simulation system: presenting spatial strategies in agents' task allocation under uncertainties

Hooshangi

Alesheikh

Panahi

et al. 2020

Preprint

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Abstract. Task allocation in uncertainty conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent- based simulation to investigate tasks allocation through the consideration of appropriate spatial strategies to deal with uncertainty in urban search and rescue (USAR) operation. The proposed method is presented in five phases: ordering existing tasks, finding coordinating agent, holding an auction, applying allocation strategies, and implementation and observation of environmental uncertainties. The methodology was evaluated in Tehran's District 1 for 6.6, 6.9, and 7.2 magnitude earthquakes. The simulation started by calculating the number of injured individuals, which was 28856, 73195 and 111463 people for each earthquake, respectively. The Simulations were performed for each scenario for a variety of rescuers (1000, 1500, 2000 rescuer). In comparison with contract net protocol (CNP), the standard time of rescue operations in the proposed approach includes at least 13% of improvement and the best percentage of recovery was 21 %. Interval uncertainty analysis and the comparison of the proposed strategies showed that an increase in uncertainty leads to an increased rescue time for CNP of 67.7 hours, and for strategies one to four an increased rescue time of 63.4, 63.2, 63.7, and 56.5 hours, respectively. Considering strategies in the task allocation process, especially spatial strategies, resulted in the optimization and increased flexibility of the allocation as well as conditions for fault tolerance and agent-based cooperation stability in USAR simulation system.

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Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing

Cited by 34 publications

References 23 publications

Application of Data Mining in the Field of Human Resource Management: A Review

Application of Data Mining in the Field of Human Resource Management: A Review

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

USAR simulation system: presenting spatial strategies in agents' task allocation under uncertainties

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