Automated task scheduling for automotive industry

R. Lewandowski; J. I. Olszewska

doi:10.1109/INES49302.2020.9147169

Automated task scheduling for automotive industry

R. Lewandowski
, J. I. Olszewska

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

233 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 language	English
Title of host publication	2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	000159-000164
Number of pages	6
ISBN (Electronic)	9781728110592
DOIs	https://doi.org/10.1109/INES49302.2020.9147169
Publication status	Published - 27 Jul 2020
Event	24th IEEE International Conference on Intelligent Engineering Systems 2020 - Online, Reykjavik, Iceland Duration: 8 Jul 2020 → 10 Jul 2020

Publication series

Name	IEEE Conference Proceedings
Publisher	IEEE
ISSN (Electronic)	1543-9259

Conference

Conference	24th IEEE International Conference on Intelligent Engineering Systems 2020
Abbreviated title	INES 2020
Country/Territory	Iceland
City	Reykjavik
Period	8/07/20 → 10/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Keywords

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

Access to Document

10.1109/INES49302.2020.9147169

2020 05 04 Lewandowski et al Automated acceptedAccepted author manuscript, 690 KB

Joanna Olszewska
Person

Cite this

@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",

}

Lewandowski, R & Olszewska, JI 2020, Automated task scheduling for automotive industry. in 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES). IEEE Conference Proceedings, IEEE, Piscataway, NJ, pp. 000159-000164, 24th IEEE International Conference on Intelligent Engineering Systems 2020, Reykjavik, Iceland, 8/07/20. https://doi.org/10.1109/INES49302.2020.9147169

TY - GEN

T1 - Automated task scheduling for automotive industry

AU - Lewandowski, R.

AU - Olszewska, J. I.

PY - 2020/7/27

Y1 - 2020/7/27

N2 - 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.

AB - 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.

KW - intelligent systems

KW - industry 4.0 and smart factory

KW - manufacturing-oriented data analytics

KW - intelligent human-machine interaction

KW - expert systems

U2 - 10.1109/INES49302.2020.9147169

DO - 10.1109/INES49302.2020.9147169

M3 - Conference contribution

T3 - IEEE Conference Proceedings

SP - 159

EP - 164

BT - 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)

PB - IEEE

CY - Piscataway, NJ

T2 - 24th IEEE International Conference on Intelligent Engineering Systems 2020

Y2 - 8 July 2020 through 10 July 2020

ER -

Automated task scheduling for automotive industry

Abstract

Publication series

Conference

UN SDGs

Keywords

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Joanna Olszewska

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