Integrated data envelopment analysis and neural network model for forecasting performance of wafer fabrication operations

Hsu, Chia‐Yu

doi:10.1007/s10845-013-0808-0

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…, n, θ unrestricted in sign. (15) After this replacement, both the inner and outer programs have the same direction of minimization; thus, they can be combined into the same one level. The resulting program of ( 10) becomes the following one-level program:…”

Section: Fuzzy Network Dea In Decision-makingmentioning

confidence: 99%

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A Fuzzy Network DEA Approach to the Selection of Advanced Manufacturing Technology

Lu¹

2021

Sustainability

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The selection of advanced manufacturing technologies (AMTs) is an essential yet complex decision that requires careful consideration of various performance criteria. In real-world applications, there are cases that observations are difficult to measure precisely, observations are represented as linguistic terms, or the data need to be estimated. Since the growth of engineering sciences has been the key reason for the increased utilization of AMTs, this paper develops a fuzzy network data envelopment analysis (DEA) to the selection of AMT alternatives considering multiple decision-makers (DMs) and weight restrictions when the input and output data are represented as fuzzy numbers. By viewing the multiple DMs as a network one, the data provided by each DM can then be taken into account in evaluating the overall performances of AMT alternatives. In the solution process, we obtain the overall and DMs efficiency scores of each AMT alternative at the same time, and a relationship in which the former is a weighted average of the latter is also derived. Since the final evaluation results of AMTs are fuzzy numbers, a ranking procedure is employed to determine the most preferred one. An example is used to illustrate the applicability of the proposed methodology.

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Section: Fuzzy Network Dea In Decision-makingmentioning

confidence: 99%

“…Several studies in the literature have applied DEA models for AMT selection and justification problems [6][7][8][9][10][11][12][13][14][15][16]. Nevertheless, these methods for AMT evaluations are considered only the case of a single decision-maker (DM).…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Network DEA Approach to the Selection of Advanced Manufacturing Technology

Lu¹

2021

Sustainability

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Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification

Hsu

Chien

2020

J Intell Manuf

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Defective wafer detection using a denoising autoencoder for semiconductor manufacturing processes

Fan

Hsu

Jen

et al. 2020

Advanced Engineering Informatics

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Integrated data envelopment analysis and neural network model for forecasting performance of wafer fabrication operations

Cited by 6 publications

References 32 publications

A Fuzzy Network DEA Approach to the Selection of Advanced Manufacturing Technology

A Fuzzy Network DEA Approach to the Selection of Advanced Manufacturing Technology

Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification

Defective wafer detection using a denoising autoencoder for semiconductor manufacturing processes

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