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Mixture Density Networks as a General Framework for Estimation and Prediction of Waiting Time Distributions in Queueing Systems

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Performance Engineering and Stochastic Modeling (EPEW 2021, ASMTA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 13104))

Abstract

We employ Mixture Density Networks (MDNs) as a general approach for estimation of customer waiting times in queueing systems based on system states that customers observe upon their arrival. We generate a large amount of data by using discrete-event simulation. Part of the generated dataset is used to train the model, and we utilize the whole dataset for the evaluation of the model. Finally, we illustrate this application in a real-world dataset.

Supported by JSPS KAKENHI Grant Number 18K18006 and 21K11765.

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Author information

Authors and Affiliations

  1. Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Hung Quoc Nguyen

  2. Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Tuan Phung-Duc

Authors
  1. Hung Quoc Nguyen
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  2. Tuan Phung-Duc
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Corresponding author

Correspondence to Tuan Phung-Duc .

Editor information

Editors and Affiliations

  1. University Paris-Saclay, Gif-sur-Yvette, France

    Paolo Ballarini

  2. Telecom Sub Paris, Evry, France

    Hind Castel

  3. University of Patras, Patras, Greece

    Ioannis Dimitriou

  4. Università degli studi della Campania Luigi Vanvitelli, Caserta, Italy

    Mauro Iacono

  5. Faculty of Engineering, Information and Systems, Division of Policy and Planning Sciences, University of Tsukuba, Tsukuba, Japan

    Tuan Phung-Duc

  6. Ghent University, Gent, Belgium

    Joris Walraevens

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Nguyen, H.Q., Phung-Duc, T. (2021). Mixture Density Networks as a General Framework for Estimation and Prediction of Waiting Time Distributions in Queueing Systems. In: Ballarini, P., Castel, H., Dimitriou, I., Iacono, M., Phung-Duc, T., Walraevens, J. (eds) Performance Engineering and Stochastic Modeling. EPEW ASMTA 2021 2021. Lecture Notes in Computer Science(), vol 13104. Springer, Cham. https://doi.org/10.1007/978-3-030-91825-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-91825-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91824-8

  • Online ISBN: 978-3-030-91825-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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