Automated task scheduling for automotive industry

R. Lewandowski, J. I. Olszewska

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    9 Citations (Scopus)
    225 Downloads (Pure)

    Abstract

    Nowadays, the automotive industry requires an increased use of intelligent systems to endure. In this paper, we present a new solution for automated task scheduling to help automotive industry in efficiently managing garage employees’ time and improving the effectiveness of the servicing and maintenance tasks of vehicles. The developed approach consists in a set of interconnected web applications with a model-view-controller based-on architecture and expert-knowledge temporal logic rules to automate the assignment of the daily workload for engineers working on multiple workstations within an automotive company. The proposed intelligent system prioritises and selects tasks for these engineers; the scheduled tasks being automatically ordered and displayed accordingly on screens visible in the related garage workstations. This automated task scheduling system has been successfully deployed within Arnold Clark Automobiles Ltd Company, and the performance of this new application used by a group of engineers under real-world operational conditions have been assessed and analysed.
    Original languageEnglish
    Title of host publication2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages000159-000164
    Number of pages6
    ISBN (Electronic)9781728110592
    DOIs
    Publication statusPublished - 27 Jul 2020
    Event24th IEEE International Conference on Intelligent Engineering Systems 2020 - Online, Reykjavik, Iceland
    Duration: 8 Jul 202010 Jul 2020

    Publication series

    NameIEEE Conference Proceedings
    PublisherIEEE
    ISSN (Electronic)1543-9259

    Conference

    Conference24th IEEE International Conference on Intelligent Engineering Systems 2020
    Abbreviated titleINES 2020
    Country/TerritoryIceland
    CityReykjavik
    Period8/07/2010/07/20

    Keywords

    • intelligent systems
    • industry 4.0 and smart factory
    • manufacturing-oriented data analytics
    • intelligent human-machine interaction
    • expert systems

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