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Dynamic maintenance priority of a real-world machining line
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-4086-3877
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-1699-3778
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-3973-3394
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
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2016 (English)In: Proceedings of the 7th Swedish Production Symposium, 2016Conference paper, Published paper (Refereed)
Abstract [en]

To support the shop-floor operators, decision support systems (DSS) are becoming more and more vital to the success of manufacturing systems in industry today. In order to get a DSS able to adapt to disturbances in a production system, on-line data are needed to be able to make optimal or near-optimal decisions in real-time (soft real-time). This paper investigates one part of such a system, i.e. how different priorities of maintenance activities (planned and unplanned) affect the productivity of a production system. A discrete-event simulation model has been built for a real-world machining line in order to simulate the decisions made in subject to disturbances. This paper presents a way of prioritizing operators and machines based on multiple criteria such as competence, utilization, distance, bottleneck, and Work-In-Process. An experimental study based on the real-world production system has shown promising results and given insights of how to prioritize the operators in a good way. Another novelty introduced in this paper is the use of simulation-based optimization to generate composite dispatching rules in order to find good tradeoffs when taking a decision of which machine or operator to select.

Place, publisher, year, edition, pages
2016.
Keywords [en]
maintenance, simulation, optimization, genetic programming
National Category
Other Engineering and Technologies
Research subject
Technology; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-13232OAI: oai:DiVA.org:his-13232DiVA, id: diva2:1056511
Conference
7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016
Projects
Young Operator2020 (YOU2)Interactive Decision Support using Simulation-based Innovization (IDSS)
Funder
Knowledge FoundationAvailable from: 2016-12-15 Created: 2016-12-14 Last updated: 2025-02-10

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fulltext(1260 kB)358 downloads
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Frantzén, MarcusHolm, MagnusSyberfeldt, AnnaNg, Amos H. C.

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CiteExportLink to record
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Citation style
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