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A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting

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Abstract

Forecasting future productivity is a critical task to every organization. However, the existing methods for productivity forecasting have two problems. First, the logarithmic or log-sigmoid value, rather than the original value, of productivity is dealt with. Second, the objective functions are not consistent with those adopted in practice. To address these problems, a fuzzy polynomial fitting and mathematical programming (FPF-MP) approach are proposed in this study. The FPF-MP approach solves two polynomial programming problems, based on the original value of productivity, in two steps to optimize accuracy and precision of forecasting future productivity, respectively. A real case was adopted to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed FPF-MP approach outperformed six existing methods in improving the forecasting accuracy and precision.

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Abbreviations

ANN:

Artificial neural network

ARIMA:

Autoregressive integrated moving average

DEA:

Data envelopment analysis

DRAM:

Dynamic random access memory

FCI:

Fuzzy collaborative intelligence

FLR:

Fuzzy linear regression

FNN:

Fuzzy neural network

FPF:

Fuzzy polynomial fitting

GMM:

Generalized method of moments

iMTA:

Institute for medical technology assessment

iPCQ:

iMTA productivity cost questionnaire

KKT:

Karush–Kuhn–Tucker

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MP:

Mathematical programming

MPI:

Malmquist productivity index

NLP:

Nonlinear programming

PP:

Polynomial programming

RMSE:

Root mean square error

STB:

Smaller-the-better

TFN:

Triangular fuzzy number

VOLP:

Valuation of lost productivity

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Acknowledgements

This study was sponsored by the Ministry of Science and Technology, Taiwan.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan

    Toly Chen

  2. Department of Industrial and Systems Engineering, Chang Yuan Christian University, 200 Chung Pei Road, Chung Li District, Taoyuan, Taiwan

    Chungwei Ou

  3. Department of Computer-aided Industrial Design, Overseas Chinese University, 100 Chiao Kung Rd., Taichung, Taiwan

    Yu-Cheng Lin

Authors
  1. Toly Chen
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  2. Chungwei Ou
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  3. Yu-Cheng Lin
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Correspondence to Chungwei Ou.

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Chen, T., Ou, C. & Lin, YC. A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting. Comput Math Organ Theory 25, 85–107 (2019). https://doi.org/10.1007/s10588-018-09287-w

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