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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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing Vol. 12; no. 2; pp. 2751 - 2763
Main Authors: Chen, Toly, Wang, Yu-Cheng, Chiu, Min-Chi
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021
Springer Nature B.V
Subjects:
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
Productivity
Fuzzy collaborative forecasting
Type-II fuzzy number
Mixed binary nonlinear programming
ISSN:1868-5137, 1868-5145
Online Access:Get full text
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Summary: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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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-020-02435-8