A type-II fuzzy collaborative forecasting approach for productivity forecasting under an uncertainty environment
Forecasting factory productivity is a critical task. However, it is not easy owing to the uncertainty of productivity. Existing methods often forecast productivity using a fuzzy number. However, the range of a fuzzy productivity forecast is wide owing to the consideration of extreme cases. In this s...
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| Published in: | Journal of ambient intelligence and humanized computing Vol. 12; no. 2; pp. 2751 - 2763 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2021
Springer Nature B.V |
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| ISSN: | 1868-5137, 1868-5145 |
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| Abstract | Forecasting factory productivity is a critical task. However, it is not easy owing to the uncertainty of productivity. Existing methods often forecast productivity using a fuzzy number. However, the range of a fuzzy productivity forecast is wide owing to the consideration of extreme cases. In this study, a fuzzy collaborative forecasting approach is proposed to forecast factory productivity using a type-II fuzzy number and by narrowing the forecast’s range. The outer section of the type-II fuzzy number determines the range of productivity, while the inner section is defuzzified to derive the most likely value. Based on the experimental results, the proposed methodology surpassed existing methods in improving forecasting precision and accuracy, with a reduction in the mean absolute percentage error (MAPE) of up to 74%. |
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| AbstractList | Forecasting factory productivity is a critical task. However, it is not easy owing to the uncertainty of productivity. Existing methods often forecast productivity using a fuzzy number. However, the range of a fuzzy productivity forecast is wide owing to the consideration of extreme cases. In this study, a fuzzy collaborative forecasting approach is proposed to forecast factory productivity using a type-II fuzzy number and by narrowing the forecast’s range. The outer section of the type-II fuzzy number determines the range of productivity, while the inner section is defuzzified to derive the most likely value. Based on the experimental results, the proposed methodology surpassed existing methods in improving forecasting precision and accuracy, with a reduction in the mean absolute percentage error (MAPE) of up to 74%. |
| Author | Chiu, Min-Chi Chen, Toly Wang, Yu-Cheng |
| Author_xml | – sequence: 1 givenname: Toly surname: Chen fullname: Chen, Toly organization: Department of Industrial Engineering and Management, National Chiao Tung University – sequence: 2 givenname: Yu-Cheng orcidid: 0000-0003-4339-1665 surname: Wang fullname: Wang, Yu-Cheng email: tony.cobra@msa.hinet.net organization: Department of Aeronautical Engineering, Chaoyang University of Technology – sequence: 3 givenname: Min-Chi surname: Chiu fullname: Chiu, Min-Chi organization: Department of Industrial Engineering and Management, National Chin-Yi University of Technology |
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| Keywords | Productivity Fuzzy collaborative forecasting Type-II fuzzy number Mixed binary nonlinear programming |
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| SubjectTerms | Accuracy Artificial Intelligence Collaboration Computational Intelligence Engineering Factories Forecasting Fuzzy systems Linear programming Literature reviews Manufacturing Methods Original Research Productivity Robotics and Automation Statistical analysis Time series Uncertainty User Interfaces and Human Computer Interaction |
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| Title | A type-II fuzzy collaborative forecasting approach for productivity forecasting under an uncertainty environment |
| URI | https://link.springer.com/article/10.1007/s12652-020-02435-8 https://www.proquest.com/docview/2919490606 |
| Volume | 12 |
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