@inproceedings{d5809da31d874849b48399ec88bc2505,
title = "Automated task scheduling for automotive industry",
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{\textquoteright} 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.",
keywords = "intelligent systems, industry 4.0 and smart factory, manufacturing-oriented data analytics, intelligent human-machine interaction, expert systems",
author = "R. Lewandowski and Olszewska, {J. I.}",
year = "2020",
month = jul,
day = "27",
doi = "10.1109/INES49302.2020.9147169",
language = "English",
series = "IEEE Conference Proceedings",
publisher = "IEEE",
pages = "000159--000164",
booktitle = "2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)",
address = "United States",
note = "24th IEEE International Conference on Intelligent Engineering Systems 2020, INES 2020 ; Conference date: 08-07-2020 Through 10-07-2020",
}
