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Fuzzy Random Regression-Based Modeling in Uncertain Environment

Fuzzy Random Regression-Based Modeling in Uncertain Environment

Nureize Arbaiy, Junzo Watada, Pei-Chun Lin
Copyright: © 2016 |Pages: 20
ISBN13: 9781466697553|ISBN10: 1466697555|EISBN13: 9781466697560
DOI: 10.4018/978-1-4666-9755-3.ch006
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MLA

Arbaiy, Nureize, et al. "Fuzzy Random Regression-Based Modeling in Uncertain Environment." Sustaining Power Resources through Energy Optimization and Engineering, edited by Pandian Vasant and Nikolai Voropai, IGI Global, 2016, pp. 127-146. https://doi.org/10.4018/978-1-4666-9755-3.ch006

APA

Arbaiy, N., Watada, J., & Lin, P. (2016). Fuzzy Random Regression-Based Modeling in Uncertain Environment. In P. Vasant & N. Voropai (Eds.), Sustaining Power Resources through Energy Optimization and Engineering (pp. 127-146). IGI Global. https://doi.org/10.4018/978-1-4666-9755-3.ch006

Chicago

Arbaiy, Nureize, Junzo Watada, and Pei-Chun Lin. "Fuzzy Random Regression-Based Modeling in Uncertain Environment." In Sustaining Power Resources through Energy Optimization and Engineering, edited by Pandian Vasant and Nikolai Voropai, 127-146. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9755-3.ch006

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Abstract

The parameter value determination is important to avoid the developed mathematical model is troublesome and may yield inappropriate results. However, estimating the weights of the parameter or objective functions in the mathematical model is sometimes not easy in real situations, especially when the values are unavailable or difficult to decide. Additionally, various uncertainties include in the statistical data makes common mathematical analysis is not competent to deal with. Hence, this paper presents the Fuzzy Random Regression approach to determine the coefficient whereby statistical data used contain uncertainties namely, fuzziness and randomness. The proposed methods are able to provide coefficient information in the model setting and consideration of uncertainties in the evaluation process. The assessment of coefficient value is given by Weight Absolute Percentage Error of Fuzzy Decision. It clarifies the results between fuzzy decision and non-fuzzy decision that shows the distance of different between both approaches. Finally, a real-life application of production planning models is provided to illustrate the applicability of the proposed algorithms to a practical case study.

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