A nonlinearly normalized back propagation network and cloud computing approach for determining cycle time allowance during wafer fabrication

Chen, Toly; Wang, Yi‐Chi

doi:10.1016/j.rcim.2015.11.005

Cited by 13 publications

(4 citation statements)

References 47 publications

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“…To keep consistent with the results in the paper [12], the raw data are normalized into [0.1, 0.9] by linear min-max normalization, and the first 90 and the last 30 data samples are used for training and testing stages, respectively. In Tables 1 and 2, eight methods in the paper [12] are listed for comparison in the first three columns of each table. For example, the hit rate (HR) of constant due date policy with an allowance of 55 hours is 55% in Table 1.…”

Section: A Comparison With Different Methodsmentioning

confidence: 99%

“…Wang et al [38] considered production management planning under the theory of constraint framework to improve the hit rate and compared the performance of three experimental scenarios of a job shop. Chen and Wang [12] framed DDM as a supervised machine learning problem, that is, a neural network was trained and predicted on training and testing datasets, respectively, and demonstrated its effectiveness over seven other policies in terms of hit rate.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…In [12], one of the case studies with 120 data samples with detailed values was collected from a wafer fab located in the Central Taiwan Science Park in Taichung, Taiwan. Six features out of 12 candidates are chosen by backward elimination regression analyses.…”

Section: Case Studymentioning

confidence: 99%

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Optimal Decision Tree for Cycle Time Prediction and Allowance Determination

Hsu

2021

IEEE Access

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The due-date quotation is a key performance indicator for managing customer orders which would influence customer acceptance and/or the future potential lateness penalty. The production cycle time and allowance time are added and used as the due date of order. The objective is to maximize the hit rate which is the percentage of the orders fulfilled within the time limit of quoted due date. Under the framework of supervised machine learning, we explore the new developments in feature selection and the optimal decision tree to predict cycle time by using mixed-integer optimization. Cycle time allowance could be added to the predicted cycle time or incorporated in an optimization problem as a managerial decision variable. Case studies are used to demonstrate the effectiveness of this approach, and their performances are comparable to the other popular ensemble tree approaches, such as random forests and gradient boosting. INDEX TERMS Allowance determination, cycle time prediction, gradient boosting, hit rate, local search in a decision tree, mixed-integer optimization, optimal decision tree, random forests.

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Section: A Comparison With Different Methodsmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

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Optimal Decision Tree for Cycle Time Prediction and Allowance Determination

Hsu

2021

IEEE Access

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“…The priority of a critical factor is usually modelled with a crisp value, rather than a fuzzy value. As a result, some meaningful information, such as the possibly highest and lowest priorities of a critical factor, is lost [18][19][20][21].…”

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confidence: 99%

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

Wang

Chen

2020

Mathematics

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The COVID-19 pandemic has severely impacted our daily lives. For tackling the COVID-19 pandemic, various intervention strategies have been adopted by country (or city) governments around the world. However, whether an intervention strategy will be successful, acceptable, and cost-effective or not is still questionable. To address this issue, a varying partial consensus fuzzy collaborative intelligence approach is proposed in this study to assess an intervention strategy. In the varying partial consensus fuzzy collaborative intelligence approach, multiple decision makers express their judgments on the relative priorities of factors critical to an intervention strategy. If decision makers lack an overall consensus, the layered partial consensus approach is applied to aggregate their judgments for each critical factor. The number of decision makers that reach a partial consensus varies from a critical factor to another. Subsequently, the generalized fuzzy weighted assessment approach is proposed to evaluate the overall performance of an intervention strategy for tackling the COVID-19 pandemic. The proposed methodology has been applied to compare 15 existing intervention strategies for tackling the COVID-19 pandemic.

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Trust-Based Chaos Access Control Framework by Neural Network for Cloud Computing Environment

Benifa

Mini

2020

Trends in Cloud-Based IoT

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A nonlinearly normalized back propagation network and cloud computing approach for determining cycle time allowance during wafer fabrication

Cited by 13 publications

References 47 publications

Optimal Decision Tree for Cycle Time Prediction and Allowance Determination

Optimal Decision Tree for Cycle Time Prediction and Allowance Determination

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

Trust-Based Chaos Access Control Framework by Neural Network for Cloud Computing Environment

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