A type-II fuzzy collaborative forecasting approach for productivity forecasting under an uncertainty environment

Chen, Toly; Wang, Yu Cheng; Chiu, Min-Chi

doi:10.1007/s12652-020-02435-8

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…The priorities of critical factor i derived for all travelers are aggregated as

, which is an interval-valued fuzzy set. 27 , 35 In addition,

has two membership functions, the LMF

and UMF

, such that

.…”

Section: Methodsmentioning

confidence: 99%

“…The priorities of critical factor i derived for all travelers are aggregated as w ~ i false( normalall false), which is an interval-valued fuzzy set. 27,35 In addition, w ~ i false( all false) has two membership functions, the LMF w ~ i l false( all false) and UMF w ~ i u false( all false), such that μ w false~ i false( x false) = false[ μ w false~ i l false( x false) , 0.25em μ w false~ i u false( x false) false].…”

Section: Methodsmentioning

confidence: 99%

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Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic

Lin

Chen

2022

DIGITAL HEALTH

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During the coronavirus disease 2019 (COVID-19) pandemic, it is difficult for travelers to choose suitable nature-based leisure travel destinations because many factors are related to health risks and are highly uncertain. This research proposes a type-II fuzzy approach with explainable artificial intelligence to overcome this difficulty. First, an innovative type-II alpha-cut operations fuzzy collaborative intelligence method was used to derive the fuzzy priorities of factors critical for nature-based leisure travel destination selection. Subsequently, a type-II fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje method, which is also novel, was employed to evaluate and compare the overall performance of nature-based leisure travel destinations. Furthermore, several measures were taken to enhance the explainability of the selection process and result. The effectiveness of the proposed type-II fuzzy approach was evaluated in a regional experiment conducted in Taichung City, Taiwan, during the COVID-19 pandemic.

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“…The priorities of critical factor i derived for all travelers are aggregated as

, which is an interval-valued fuzzy set. 27 , 35 In addition,

has two membership functions, the LMF

and UMF

, such that

.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic

Lin

Chen

2022

DIGITAL HEALTH

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“…Productivity is an important and vital issue at the level of the factory, organization, and even the country (T. Chen et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Massy and Wilger (1995) define productivity as the ratio of outputs to inputs or benefits to costs (Fairweather, 2002). Although many factors affect the increase or decrease of productivity, predicting the most important factors affecting the productivity of an organization or factory is a challenging task (T. Chen et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of Collective Decision-Making on Productivity: A Structural Equation Modeling

Mirbagheri,

Rafiei Atani,

Parsanejad

2023

SAGE Open

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The purpose of this study is to examine the relationship between common rationality (as a factor of convergence of employees and managers), collective decision-making (as one of the important methods of participation), and productivity (as an important indicator of organizational success). Structural equation modeling (SEM) is used to investigate the strength of these relationships. After the distribution of 98 questionnaires among organizational experts we used Smart PLS 2.0 to analyze the data. The results show that the indirect relationship between common rationality and productivity through a mediating variable of collective decision-making is stronger than the direct effect of common rationality on productivity. It means that common rationality as a substantial asset for an organization could be used to improve productivity but if managers want to utilize this asset, they should apply it through another mediator which is collective decision-making. Collective decision-making could facilitate the relationship between common rationality and productivity by creating synergy among employees, reducing biases, increasing decision quality, and reaching the optimal decision.

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“…Chen et al [12] proposed a fuzzy polynomial fitting and mathematical programming approach. Chen et al [14] considered the uncertainty of productivity with a type-II fuzzy number, and proposed a type-II fuzzy collaborative forecasting approach for productivity forecasting. In the view of Chen and Lin [11], acquiring diversified three-dimensional (3D) printers can increase the productivity of a manufacturer.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

Chen

Lin

2021

Complex Intell. Syst.

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Most existing methods for forecasting the productivity of a factory cannot estimate the range of productivity reliably, especially when future conditions are distinct from those in the past. To address this issue, a fuzzified feedforward neural network (FFNN) approach is proposed in this study. The FFNN approach improves the forecasting precision after generating accurate fuzzy productivity forecasts. In addition, the acceptable range of a fuzzy productivity forecast is specified, based on which the sum of the memberships of actual values is maximized. In this way, the range of productivity can be precisely estimated. After applying the FFNN approach to a real case, the experimental results revealed the superiority of the FFNN approach by improving the forecasting precision, in terms of the hit rate, by 25%. Such an improvement also contributed to a better forecasting accuracy. The superiority of the FFNN approach is in the context that the accuracy of forecasting productivity is optimized only after the range of productivity has been precisely estimated. In contrast, most state-of-the-art methods focus on optimizing the forecasting accuracy, but may be ineffective without information about the range of productivity when future conditions are distinct from the past.

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A type-II fuzzy collaborative forecasting approach for productivity forecasting under an uncertainty environment

Cited by 10 publications

References 29 publications

Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic

Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic

The Effect of Collective Decision-Making on Productivity: A Structural Equation Modeling

Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

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