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Applying Aggregated Line Modeling Techniques to Optimize Real World Manufacturing Systems
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Volvo Car Corporation, Skövde, Sweden. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-1215-152x
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-4086-3877
2018 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 89-96Article in journal (Refereed) Published
Abstract [en]

The application of discrete event simulation methodology in the analysis of higher level manufacturing systems has been limited due to model complexity and the lack of aggregation techniques for manufacturing lines. Recent research has introduced new aggregation methods preparing for new approaches in the analysis of higher level manufacturing systems or networks. In this paper one of the new aggregated line modeling techniques is successfully applied on a real world manufacturing system, solving a real-world problem. The results demonstrate that the aggregation technique is adequate to be applied in plant wide models. Furthermore, in this particular case, there is a potential to reduce storage levels by over 25 %, through leveling the production flow, without compromising deliveries to customers.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 25, p. 89-96
Keywords [en]
Discrete event simulation, Aggregated line modeling, Multi-objective optimization, Manufacturing systems
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
INF203 Virtual Machining; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16476DOI: 10.1016/j.promfg.2018.06.061ISI: 000547903500012Scopus ID: 2-s2.0-85062632645OAI: oai:DiVA.org:his-16476DiVA, id: diva2:1278896
Conference
8th Swedish Production Symposium, SPS, Stockholm, Sweden May 16-18, 2018
Funder
Knowledge Foundation
Note

CC BY-NC-ND 4.0

Available from: 2019-01-15 Created: 2019-01-15 Last updated: 2024-05-17Bibliographically approved
In thesis
1. Evaluating Fast and Efficient Modeling Methods for Simulation-Based Optimization
Open this publication in new window or tab >>Evaluating Fast and Efficient Modeling Methods for Simulation-Based Optimization
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As the industry in general, and the automotive industry in particular, is transforming -- due to new technologies and changes in market demands through electrification, digitalization, and globalization -- maintaining a competitive edge will require better predictions. Better predictions of production performance allows companies to capitalize on opportunities, avoid costly mistakes, and be proactive about change.

A commonly used tool in manufacturing for the prediction of production performance is discrete-event simulation. In combination with artificial intelligence methods such as multi-objective optimization, in literature often referred to as simulation-based optimization, and knowledge extraction, bottlenecks in the production process can be identified and recipes for optimal improvement order can be obtained. These recipes support the decision-maker in both understanding the production system and improving it optimally in terms of resource efficiency and investment cost. Even though the use of simulation-based optimization is widespread on the production line level, use on the factory level is more scarce. Improvements on the production line level, without a holistic view of factory performance, can be suboptimal and may only lead to increased storage levels instead of increased output to the customer.

The main obstacle for applying simulation-based optimization to the factory level is the complexity of its constituent parts, i.e., detailed production line models. Connecting several detailed production line models to create a factory model results in an overly complicated, albeit, accurate model. A single factory model running for one minute would equate to almost 140 days required for an optimization project, too long to provide decision-support relevant to manufacturing decision-making. This can be mitigated by parallel computing, but a more effective approach is to simplify the production line models to decrease the runtime while trying to maintain accuracy. Model simplification methods are approaches to reduce model complexity in new and existing simulation models. Previous research has provided an accurate and runtime efficient simplification method by use of a generic model structure built by common modeling components. Although the method seems promising in a few publications, it was lacking external and internal validity.

This project presents simulation-based optimization on the factory level enabled by a model simplification method. By following the design science research methodology, several case-studies mainly in the automotive industry identify issues with the current implementation, propose additions to the method, and validates them.

Abstract [sv]

Industrin i allmänhet, och fordonsindustrin i synnerhet, är under transformation -- som reaktion på nya tekniker och marknadsförändringar orsakade av elektrifiering, digitalisering och globalisering -- och för att upprätthålla konkurrenskraft krävs bättre prediktering av nuvarande och framtida produktionsprestanda. Bättre prediktering ger företag möjlighet att gå från att vara reaktiva till att vara proaktiva och undvika kostsamma misstag.       

Ett vanligt verktyg i tillverkande industri för att förutspå produktionsprestanda är diskret händelsestyrd simulering. Kombinerat med artificiell intelligens -- genom flermålsoptimering, även kallat simuleringsbaserad optimering, och kunskapsextrahering -- kan flaskhalsar identifieras och ge recept för en optimal förbättringsordning. Dessa recept stödjer beslutsfattaren genom både förståelse för sitt eget produktionssystem och hur det kan förbättras optimalt med avseende på resurseffektivitet och investeringskostnad. Även om simuleringsbaserad optimering är vanligt förekommande för produktionslinjer är det desto mer sällsynt på fabriksnivån. Förbättringar på produktionslinjenivå, utan hänsyn till den övergripande fabriksnivån, kan vara suboptimala och endast leda till ökade lagernivåer istället för leverans till slutkund.       

Det största hindret för att applicera simuleringsbaserad optimering på fabriksnivån är komplexiteten av dess ingående delar, det vill säga de detaljerade modellerna på produktionslinjenivå. Att ansluta flera detaljerade produktionslinjemodeller för att skapa en fabriksmodell resulterar i en överkomplicerad men exakt modell. En fabriksmodell som kräver en minut simuleringstid betyder nästan 140 dagars simuleringstid för en optimeringsstudie. Tiden kan minskas genom parallella processer men ett mer lämpligt angreppssätt är att minska komplexiteten för produktionslinjemodellerna, och därmed körtiden, och samtidigt bibehålla noggrannheten i resultaten. Metoder för modellförenkling är angreppssätt för att reducera modellkomplexitet i nya och existerande modeller. En existerande metod ger noggranna och snabba modeller genom en generisk modellstruktur byggd på vanligt förekommande modellkomponenter. Metoden har visats fungera i ett antal publikationer men experiment för att säkerställa validitet och generaliserbarhet saknas.       

Detta projekt presenterar simuleringsbaserad optimering på fabriker vilket möjliggörs genom en validerad modellförenklingsmetod. Genom att följa forskningsmetoden design science har flera fallstudier planerats och genomförts för att identifiera brister i nuvarande implementation, genomföra förbättringar och validera dessa.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2021. p. 140
Series
Dissertation Series ; 40
National Category
Computer Sciences
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-20624 (URN)978-91-984919-4-4 (ISBN)
Presentation
2021-10-26, Portalen, Insikten, Kanikegränd 3, Skövde, 10:00 (English)
Opponent
Supervisors
Projects
Smart Industry Research School
Funder
Knowledge Foundation
Note

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of University of Skövde's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.

Available from: 2021-10-12 Created: 2021-10-06 Last updated: 2021-11-01Bibliographically approved

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Lidberg, SimonPehrsson, LeifFrantzén, Marcus

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