Evaluation System and Correlation Analysis for Determining the Performance of a Semiconductor Manufacturing System

Yu, Qingyun; Li, Li; Zhao, Hui; Li, Ying; Lin, Kwei-Jay

doi:10.23919/csms.2021.0015

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…Combined with the actual situation of the semiconductor production line and the needs of the enterprise, this paper selects four performance indicators as the objectives of scheduling optimization: average CT of the workpiece, ODR of the workpiece, EA, and RM of the scheduling scheme 30 . To analyze the effectiveness of the scheduling system proposed in this paper, we design simulation experiments from multiple dimensions.…”

Section: Simulation Experimentsmentioning

confidence: 99%

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

Yu,

Zhang,

Zhao

et al. 2023

Sci Rep

Self Cite

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A robust optimal scheduling method driven by multi-objects is proposed for the collaborative optimization problem between dynamic scheduling, preventive maintenance of equipment, and robustness of scheduling schemes in a complex manufacturing system. Firstly, the equipment maintenance task is mapped to the process level, and composite dispatching rules with weight parameters are designed, which flexibly consider equipment maintenance and system processing status. Secondly, the performance-driven ideology is analyzed through two models based on the IWOA-MLP algorithm. Thirdly, the feedback mechanism ideology facilitates adaptive closed-loop optimizations. Finally, a series of experiments were carried out on the simulation platform of a semiconductor manufacturing enterprise in Shanghai. The experimental results show that the proposed robust optimal scheduling system can effectively deal with mixed uncertainty, improve production performances, and maintain highly robust measures.

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Section: Simulation Experimentsmentioning

confidence: 99%

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

Yu,

Zhang,

Zhao

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to better analyze the management of college students by emotional management and incentive methods, this paper mainly compares the emotional management and incentive management of college students with the traditional college student management ( Yu et al, 2021 ). Therefore, the selection of experimental samples is very important.…”

Section: College Students’ Emotional Management and Motivation Manage...mentioning

confidence: 99%

Management work mode of college students based on emotional management and incentives

Ding

2022

Front. Psychol.

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The student management work model in colleges and universities is an effective plan for college student management, but the traditional college student management work is not very good in terms of student psychology, resulting in negative attitudes such as low learning desire, low learning efficiency, and inactive learning. In recent years, with the development of artificial intelligence technologies such as sentiment analysis and incentive theory, emotional management and incentive theory have been applied to the management of college students. The emotional management and incentive model is a way to help college students get rid of psychological obstacles and guide students to establish positive and correct values by predict and analyze the psychological state of college students through language emotion recognition and BP neural network. This paper compares the college student management work model based on emotional management and incentives with the traditional college management work mode through experiments. The results show that the students’ learning enthusiasm is better than the traditional college student management work mode based on emotional management and incentives. The student management work model in colleges and universities is 15.8% better, and the students’ grades have improved by 12.5%; the college student management work model based on emotional management and incentives also has a positive role in helping students’ mental health. The way of emotional management and motivation can make better use of college students’ psychology to effectively manage students and guide students to develop in a good direction.

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“…When selecting the psychological state prediction and analysis indicators for evaluating leaders' attitude toward employees' work, it is necessary to carry out a correlation analysis between the above-mentioned influencing indicators and the psychological state prediction analysis of leaders' attitude toward employees' work (Yu et al, 2021). The correlation analysis of the samples is to prevent the failure of the experiment due to improper selection of samples.…”

Section: Correlation Analysis Of Samplesmentioning

confidence: 99%

Predictive analysis of the psychological state of charismatic leaders on employees' work attitudes based on artificial intelligence affective computing

Liu

Song²

2022

Front. Psychol.

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With the progress of social production, the competition for talents among enterprises is fierce, and the market often lacks capable leaders, which leads to the lack of management of enterprise employees and cannot bring more economic benefits to enterprises. Traditional leaders make subordinate employees work actively and achieve the common goal of the enterprise by exerting their own leadership characteristics and observing their subordinates, but they cannot take care of the psychological state of each employee, resulting in the employee's work efficiency is not very high. In recent years, charismatic leadership has become an important economic leader in the new era, and the theoretical spirit of charismatic leadership can well guide employees to work actively. Artificial intelligence affective computing can well identify the psychological state of the subject, and the combination of artificial intelligence affective computing and charismatic leadership can achieve effective management of employees through the predictive analysis of employees' psychological state. This paper compares the psychological state prediction analysis of employees' work attitudes between charismatic leaders based on artificial intelligence affective computing and traditional leaders through experiments. The results show that: charismatic leaders based on artificial intelligence affective computing predictive analytics can improve sensitivity to employee needs, resulting in an 8.2% increase in employee trust in leadership, a 4.4% increase in employee commitment to achieving organizational goals, and a 19.3% increase in employee job satisfaction. The psychological state prediction analysis of charismatic leaders based on artificial intelligence affective computing on employees' work attitudes can improve the work efficiency of employees and the economic benefits of enterprises.

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Evaluation System and Correlation Analysis for Determining the Performance of a Semiconductor Manufacturing System

Cited by 10 publications

References 23 publications

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

Management work mode of college students based on emotional management and incentives

Predictive analysis of the psychological state of charismatic leaders on employees' work attitudes based on artificial intelligence affective computing

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