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Module for Prediction of Technological Operation Times in an Intelligent Job Scheduling System

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Intelligent Systems in Production Engineering and Maintenance (ISPEM 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 835))

Abstract

This paper presents a model for the prediction of technological operation times in the framework of an intelligent job scheduling system. The developed prediction module implements ARMA/ARIMA time series models. In addition, the paper introduces the mathematical prediction model and its implementation to the particular test case. The scheduling made use of dispatching rules: LPT, SPT, FCFS and EDD. The validation of the model appears to confirm the effectiveness of the proposed solution and substantiate further research works in this direction.

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Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering, Institute of Technological Systems of Information, Lublin University of Technology, Lublin, Poland

    Łukasz Sobaszek & Arkadiusz Gola

  2. Faculty of Management, Department of Quantitative Methods in Management, Lublin University of Technology, Lublin, Poland

    Edward Kozłowski

Authors
  1. Łukasz Sobaszek
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  2. Arkadiusz Gola
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  3. Edward Kozłowski
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Corresponding author

Correspondence to Arkadiusz Gola .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Wrocław, Poland

    Anna Burduk

  2. Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Wrocław, Poland

    Edward Chlebus

  3. Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Wrocław, Poland

    Tomasz Nowakowski

  4. Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Wrocław, Poland

    Agnieszka Tubis

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Sobaszek, Ł., Gola, A., Kozłowski, E. (2019). Module for Prediction of Technological Operation Times in an Intelligent Job Scheduling System. In: Burduk, A., Chlebus, E., Nowakowski, T., Tubis, A. (eds) Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing, vol 835. Springer, Cham. https://doi.org/10.1007/978-3-319-97490-3_23

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