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Integrated buffer monitoring and control based on grey neural network

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  • Junguang Zhang
  • Dan Wan

Abstract

Classic buffer monitoring methods only consider the buffer consumption amount, while the subsequent trend information of buffer consumption is ignored. In this paper, we propose an integrated buffer monitoring method. First, the prediction model based on grey neural network is established, and the follow-up buffer consumption is predicted quantitatively according to the past and present performance data at the project monitoring points. Second, considering the relationship between the buffered consumed and the follow-up buffer consumption, a buffer integrated monitoring system is formed based on the integrated quantitative analysis on the buffer consumed and the subsequent trend information at each monitoring point. Finally, the Monte Carlo simulation experiment is carried out to validate the model system. The results show that, as opposed to the classic buffer monitoring methods, the proposed method can control the progress of the project comprehensively and reduce the fluctuation of project duration, thus achieving the double optimisation of project duration and cost under the premise of guaranteeing the completion probability.

Suggested Citation

  • Junguang Zhang & Dan Wan, 2019. "Integrated buffer monitoring and control based on grey neural network," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(3), pages 516-529, March.
  • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:3:p:516-529
    DOI: 10.1080/01605682.2018.1447251
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    Cited by:

    1. Junguang Zhang & Dan Wan, 2021. "Determination of early warning time window for bottleneck resource buffer," Annals of Operations Research, Springer, vol. 300(1), pages 289-305, May.

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