Högskolan i Skövde

his.sePublications
Change search
Link to record
Permanent link

Direct link
Lidberg, Simon, MSc.ORCID iD iconorcid.org/0000-0003-1215-152X
Publications (10 of 13) Show all publications
Lidberg, S. & Ng, A. (2023). Reproducible decision support for industrial decision making using a knowledge extraction platform on multi-objective optimization data. International Journal of Manufacturing Research, 18(4), 454-480
Open this publication in new window or tab >>Reproducible decision support for industrial decision making using a knowledge extraction platform on multi-objective optimization data
2023 (English)In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 18, no 4, p. 454-480Article in journal (Refereed) Published
Abstract [en]

Simulation-based optimisation enables companies to take decisions based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, effectively visualising and extracting knowledge from the vast amounts of data generated by many-objective optimisation algorithms can be challenging. We present an open-source, web-based application in the R language to extract knowledge from data generated from simulation-based optimisation. For the tool to be useful for real-world industrial decision-making support, several decision makers gave their requirements for such a tool. This information was used to augment the tool to provide the desired features for decision support in the industry. The open-source tool is then used to extract knowledge from two industrial use cases. Furthermore, we discuss future work, including planned additions to the open-source tool and the exploration of automatic model generation.

Place, publisher, year, edition, pages
InderScience Publishers, 2023
Keywords
knowledge-extraction, reproducible science, simulation-based optimisation, industrial use-case, decision-support, knowledge-driven optimisation
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences Software Engineering
Research subject
VF-KDO; Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23078 (URN)10.1504/IJMR.2023.135645 (DOI)001128775300001 ()2-s2.0-85180929057 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Alternativ/tidigare DOI: 10.1504/ijmr.2024.10057049

Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2024-02-14Bibliographically approved
Lidberg, S., Frantzén, M., Aslam, T. & Ng, A. H. C. (2022). A Knowledge Extraction Platform for Reproducible Decision-Support from Multi-Objective Optimization Data. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 725-736). Amsterdam; Berlin; Washington, DC: IOS Press
Open this publication in new window or tab >>A Knowledge Extraction Platform for Reproducible Decision-Support from Multi-Objective Optimization Data
2022 (English)In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 725-736Conference paper, Published paper (Refereed)
Abstract [en]

Simulation and optimization enables companies to take decision based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, it can be difficult to visualize and extract knowledge from the large amounts of data generated by a many-objective optimization genetic algorithm, especially with conflicting objectives. Existing tools offer capabilities for extracting knowledge in the form of clusters, rules, and connections. Although powerful, most existing software is proprietary and is therefore difficult to obtain, modify, and deploy, as well as for facilitating a reproducible workflow. We propose an open-source web-based application using commonly available packages in the R programming language to extract knowledge from data generated from simulation-based optimization. This application is then verified by replicating the experimental methodology of a peer-reviewed paper on knowledge extraction. Finally, further work is also discussed, focusing on method improvements and reproducible results.

Place, publisher, year, edition, pages
Amsterdam; Berlin; Washington, DC: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
multi-objective optimization, knowledge extraction, industry 4.0, decision-support, industrial optimization
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21115 (URN)10.3233/ATDE220191 (DOI)2-s2.0-85132829202 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022
Note

CC BY-NC 4.0

Corresponding Author: Simon Lidberg, Högskolevägen, BOX 1231, Skövde, Sweden; E-mail: simon.lidberg@his.se

Available from: 2022-05-04 Created: 2022-05-04 Last updated: 2023-02-22Bibliographically approved
Lidberg, S. (2021). Evaluating Fast and Efficient Modeling Methods for Simulation-Based Optimization. (Licentiate dissertation). Skövde: University of Skövde
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
Lidberg, S., Aslam, T. & Ng, A. H. C. (2020). Multi-Level Optimization with Aggregated Discrete-Event Models. In: K.-H. Bae; B. Feng; S. Kim; S. Lazarova-Molnar; Z. Zheng; T. Roeder; R. Thiesing (Ed.), Proceedings of the 2020 Winter Simulation Conference: . Paper presented at Winter Simulation Conference, December 14-18, 2020, Virtual Conference (pp. 1515-1526). IEEE
Open this publication in new window or tab >>Multi-Level Optimization with Aggregated Discrete-Event Models
2020 (English)In: Proceedings of the 2020 Winter Simulation Conference / [ed] K.-H. Bae; B. Feng; S. Kim; S. Lazarova-Molnar; Z. Zheng; T. Roeder; R. Thiesing, IEEE, 2020, p. 1515-1526Conference paper, Published paper (Refereed)
Abstract [en]

Removing bottlenecks that restrain the overall performance of a factory can give companies a competitive edge. Although in principle, it is possible to connect multiple detailed discrete-event simulation models to form a complete factory model, it could be too computationally expensive, especially if the connected models are used for simulation-based optimizations. Observing that computational speed of running a simulation model can be significantly reduced by aggregating multiple line-level models into an aggregated factory level, this paper investigates, with some loss of detail, if the identified bottleneck information from an aggregated factory model, in terms of which parameters to improve, would be useful and accurate enough when compared to the bottleneck information obtained with some detailed connected line-level models. The results from a real-world, multi-level industrial application study have demonstrated the feasibility of this approach, showing that the aggregation method can represent the underlying detailed line-level model for bottleneck analysis.

Place, publisher, year, edition, pages
IEEE, 2020
Series
Proceedings of the Winter Simulation Conference, ISSN 0891-7736, E-ISSN 1558-4305
Keywords
Aggregation, Data mining, Decision support, Discrete event simulation, Industrial case study, Multi-objective optimization, Agglomeration, Decision making, Decision support systems, Decision trees, Digital storage, Multiobjective optimization, Trees (mathematics), Decision supports, Decision-tree algorithm, Industrial problem, Industrial systems, Knowledge extraction, Real-world optimization, Simulation-based optimizations
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
VF-KDO; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-19643 (URN)10.1109/WSC48552.2020.9383990 (DOI)000679196301044 ()2-s2.0-85103904651 (Scopus ID)978-1-7281-9499-8 (ISBN)978-1-7281-9500-1 (ISBN)
Conference
Winter Simulation Conference, December 14-18, 2020, Virtual Conference
Funder
Knowledge Foundation
Note

Copyright © 2020, IEEE

Available from: 2021-04-20 Created: 2021-04-20 Last updated: 2021-10-06Bibliographically approved
Lidberg, S., Aslam, T., Pehrsson, L. & Ng, A. H. C. (2020). Optimizing real-world factory flows using aggregated discrete event simulation modelling: Creating decision-support through simulation-based optimization and knowledge-extraction. Flexible Services and Manufacturing Journal, 32(4), 888-912
Open this publication in new window or tab >>Optimizing real-world factory flows using aggregated discrete event simulation modelling: Creating decision-support through simulation-based optimization and knowledge-extraction
2020 (English)In: Flexible Services and Manufacturing Journal, ISSN 1936-6582, E-ISSN 1936-6590, Vol. 32, no 4, p. 888-912Article in journal (Refereed) Published
Abstract [en]

Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition. 

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Aggregation, Data mining, Decision support, Discrete event simulation, Industrial case study, Multi-objective optimization, Agglomeration, Decision making, Decision support systems, Decision trees, Digital storage, Multiobjective optimization, Trees (mathematics), Decision supports, Decision-tree algorithm, Industrial problem, Industrial systems, Knowledge extraction, Real-world optimization, Simulation-based optimizations
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-17480 (URN)10.1007/s10696-019-09362-7 (DOI)000591563100006 ()2-s2.0-85068764729 (Scopus ID)
Note

CC BY 4.0

Available from: 2019-07-25 Created: 2019-07-25 Last updated: 2023-02-22Bibliographically approved
Lidberg, S. (2018). Aggregated Models Enabling Optimization of Production Networks: Evaluating fast and efficient techniques for aggregation of detailed model data. Skövde: University of Skövde
Open this publication in new window or tab >>Aggregated Models Enabling Optimization of Production Networks: Evaluating fast and efficient techniques for aggregation of detailed model data
2018 (English)Report (Other (popular science, discussion, etc.))
Abstract [en]

Obtaining data-based decision support faster is a competitive advantage for companies. Faster decisions means that companies can capitalize on opportunities and avert costly mistakes. Simulation as a predictive tool, with the rise of digitalization, is used across many disciplines in the manufacturing industry. When analyzing current and future production lines, more companies are using discrete event simulation software which offers improved results and accuracy compared to static analysis tools. If simulation is coupled with multi-objective optimization and knowledge extraction, new possibilities for production systems are introduced where artificial intelligence can be used to improve the systems. 

Seeking predictive answers on the manufacturing network level by re-using line models is problematic. Simulation models on the line level are usually detailed to answer specific questions about that production line. Trying to connect several of these models with the intent to optimize the complete manufacturing network will create computationally expensive models. Reducing the complexity of the line models is not enough, a new representation of the line models is needed. New methods for the aggregation of detailed line model data into a faster and more computationally efficient line modules will enable analysis and optimization of manufacturing networks.

A novel method for the aggregation of detailed simulation model data to a more efficient meta-model is one part of the expected result for this project. Increasing the generalizability of the method is critical and application studies will be performed on different types of manufacturing processes. After the generalizability has been verified, extending the method to also incorporate the possibility of optimization and knowledge extraction, together with which input data is required, a framework can be created. This aggregation framework will be the main contribution from this project.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2018. p. 18
Keywords
Discrete-event simulation, model simplification, aggregation, complexity reduction
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-19447 (URN)
Funder
Knowledge Foundation
Note

Research proposal, PhD programme, University of Skövde

Available from: 2021-02-03 Created: 2021-02-03 Last updated: 2023-01-10Bibliographically approved
Lidberg, S., Pehrsson, L. & Frantzén, M. (2018). Applying Aggregated Line Modeling Techniques to Optimize Real World Manufacturing Systems. Paper presented at 8th Swedish Production Symposium, SPS, Stockholm, Sweden May 16-18, 2018. Procedia Manufacturing, 25, 89-96
Open this publication in new window or tab >>Applying Aggregated Line Modeling Techniques to Optimize Real World Manufacturing Systems
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
Keywords
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:nbn:se:his:diva-16476 (URN)10.1016/j.promfg.2018.06.061 (DOI)2-s2.0-85062632645 (Scopus ID)
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: 2021-10-08Bibliographically approved
Lidberg, S., Aslam, T., Pehrsson, L. & Ng, A. H. C. (2018). Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model. In: Peter Thorvald, Keith Case (Ed.), Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden. Paper presented at 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden (pp. 467-472). Amsterdam: IOS Press
Open this publication in new window or tab >>Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model
2018 (English)In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 467-472Conference paper, Published paper (Refereed)
Abstract [en]

Analyzing networks of supply-chains, where each chain is comprised of several actors with different purposes and performance measures, is a difficult task. There exists a large potential in optimizing supply-chains for many companies and therefore the supply-chain optimization problem is of great interest to study. To be able to optimize the supply-chain on a global scale, fast models are needed to reduce computational time. Previous research has been made into the aggregation of factories, but the technique has not been tested against supply-chain problems. When evaluating the configuration of factories and their inter-transportation on a global scale, new insights can be gained about which parameters are important and how the aggregation fits to a supply-chain problem. The paper presents an interactive proof-of-concept model enabling testing of supply chain concepts by users and decision makers.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
Aggregated modeling, Discrete Event Simulation, Manufacturing, Proof-of-concept, Supply-chain management, Decision making, Iterative methods, Manufacture, Supply chain management, Computational time, Global supply chain, Interactive proofs, Iterative approach, Performance measure, Proof of concept, Supply chain optimization, Industrial research
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; INF203 Virtual Machining
Identifiers
urn:nbn:se:his:diva-16496 (URN)10.3233/978-1-61499-902-7-467 (DOI)000462212700075 ()2-s2.0-85057354809 (Scopus ID)978-1-61499-901-0 (ISBN)978-1-61499-902-7 (ISBN)
Conference
16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden
Funder
Knowledge Foundation
Available from: 2018-12-13 Created: 2018-12-13 Last updated: 2021-10-06Bibliographically approved
Lidberg, S., Pehrsson, L. & Ng, A. H. C. (2018). Using Aggregated Discrete Event Simulation Models and Multi-Objective Optimization to Improve Real-World Factories. In: M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, B. Johansson (Ed.), Proceedings of the 2018 Winter Simulation Conference: . Paper presented at Winter Simulation Conference, Gothenburg, Sweden, Decemeber 9-12, 2018 (pp. 2015-2024). IEEE
Open this publication in new window or tab >>Using Aggregated Discrete Event Simulation Models and Multi-Objective Optimization to Improve Real-World Factories
2018 (English)In: Proceedings of the 2018 Winter Simulation Conference / [ed] M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, B. Johansson, IEEE, 2018, p. 2015-2024Conference paper, Published paper (Refereed)
Abstract [en]

Improving production line performance and identifying bottlenecks using simulation-based optimization has been shown to be an effective approach. Nevertheless, for larger production systems which are consisted of multiple production lines, using simulation-based optimization can be too computationally expensive, due to the complexity of the models. Previous research has shown promising techniques for aggregating production line data into computationally efficient modules, which enables the simulation of higher-level systems, i.e., factories. This paper shows how a real-world factory flow can be optimized by applying the previously mentioned aggregation techniques in combination with multi-objective optimization using an experimental approach. The particular case studied in this paper reveals potential reductions of storage levels by over 30 %, lead time reductions by 67 %, and batch sizes reduced by more than 50 % while maintaining the delivery precision of the industrial system.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Winter Simulation Conference. Proceedings., ISSN 0891-7736, E-ISSN 1558-4305
Keywords
nondominated sorting approach, algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
INF203 Virtual Machining; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16477 (URN)10.1109/WSC.2018.8632337 (DOI)000461414102019 ()2-s2.0-85062618810 (Scopus ID)978-1-5386-6572-5 (ISBN)978-1-5386-6573-2 (ISBN)978-1-5386-6570-1 (ISBN)978-1-5386-6571-8 (ISBN)
Conference
Winter Simulation Conference, Gothenburg, Sweden, Decemeber 9-12, 2018
Funder
Knowledge Foundation, 20120066
Available from: 2019-02-19 Created: 2019-02-19 Last updated: 2021-10-06
Barrera Diaz, C. A., Oscarsson, J., Lidberg, S. & Sellgren, T. (2017). A Study of Discrete Event Simulation Project Data and Provenance Information Management in an Automotive Manufacturing Plant. In: W. K. V. Chan; A. D’Ambrogio; G. Zacharewicz; N. Mustafee; G. Wainer; E. Page (Ed.), Proceedings of the 2017 Winter Simulation Conference: . Paper presented at 2017 Winter Simulation Conference, Las Vegas, December 3-6, 2017 (pp. 4012-4023). IEEE
Open this publication in new window or tab >>A Study of Discrete Event Simulation Project Data and Provenance Information Management in an Automotive Manufacturing Plant
2017 (English)In: Proceedings of the 2017 Winter Simulation Conference / [ed] W. K. V. Chan; A. D’Ambrogio; G. Zacharewicz; N. Mustafee; G. Wainer; E. Page, IEEE, 2017, , p. 12p. 4012-4023Conference paper, Published paper (Refereed)
Abstract [en]

Discrete Event Simulation (DES) project data management is a complex and important engineering activity which impacts on an organization’s efficiency. This efficiency could be decreased by the lack of provenance information or the unreliability of existing information regarding previous simulation projects, all of which complicates the reusability of the existing data. This study presents an analysis of the management of simulation projects and their provenance data, according to the different types of scenarios usually found at a manufacturing plant. A survey based on simulation projects at an automotive manufacturing plant was conducted, in order to categorize the information regarding the studied projects, map the available provenance data and standardize its management. This study also introduces an approach that demonstrates how a structured framework based on the specific data involved in the different types of scenarios could allow an improvement of the management of DES projects.

Place, publisher, year, edition, pages
IEEE, 2017. p. 12
Series
Winter Simulation Conference (WSC), E-ISSN 1558-4305 ; 2017
National Category
Engineering and Technology Communication Systems Control Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-14579 (URN)10.1109/WSC.2017.8248110 (DOI)000427768604020 ()2-s2.0-85044506370 (Scopus ID)978-1-5386-3428-8 (ISBN)978-1-5386-3429-5 (ISBN)978-1-5386-3430-1 (ISBN)
Conference
2017 Winter Simulation Conference, Las Vegas, December 3-6, 2017
Available from: 2017-12-14 Created: 2017-12-14 Last updated: 2023-07-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1215-152X

Search in DiVA

Show all publications