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Publications (7 of 7) Show all publications
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
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: 2018-05-24Bibliographically approved
Iriondo, A., Oscarsson, J. & Jeusfeld, M. A. (2017). Simulation Data Management in a Product Lifecycle Management Context. In: James Gao, Mohammed El Souri, Simeon Keates (Ed.), James Gao, Mohammed El Souri, Simeon Keates (Ed.), Advances in Manufacturing Technology XXXI: Proceedings of the 15th International Conference on Manufacturing Research, Incorporating the 32nd National Conference on Manufacturing Research, September 5–7, 2017, University of Greenwich, UK. Paper presented at 15th International Conference on Manufacturing Research ICMR 2017. Incorporating the 32nd National Conference on Manufacturing Research. University of Greenwich, London, September 5-7, 2017 (pp. 476-481). Amsterdam: IOS Press
Open this publication in new window or tab >>Simulation Data Management in a Product Lifecycle Management Context
2017 (English)In: Advances in Manufacturing Technology XXXI: Proceedings of the 15th International Conference on Manufacturing Research, Incorporating the 32nd National Conference on Manufacturing Research, September 5–7, 2017, University of Greenwich, UK / [ed] James Gao, Mohammed El Souri, Simeon Keates, Amsterdam: IOS Press, 2017, p. 476-481Conference paper, Published paper (Refereed)
Abstract [en]

Reuse of virtual engineering models and simulations improves engineering efficiency. Reuse requires preserving the information provenance. This paper suggests a framework based on the 7W data provenance model to be part of simulation data management implemented in product lifecycle management systems. The resulting provenance framework is based on a case study in which a product was re-engineered using finite element analysis.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2017
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 6
Keyword
Provenance, Virtual Engineering, Product Lifecycle Management
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; Information Systems; User Centred Product Design
Identifiers
urn:nbn:se:his:diva-14502 (URN)10.3233/978-1-61499-792-4-476 (DOI)2-s2.0-85028424715 (Scopus ID)978-1-61499-792-4 (ISBN)978-1-61499-791-7 (ISBN)978-1-61499-439-8 (ISBN)
Conference
15th International Conference on Manufacturing Research ICMR 2017. Incorporating the 32nd National Conference on Manufacturing Research. University of Greenwich, London, September 5-7, 2017
Funder
Knowledge Foundation
Available from: 2017-11-21 Created: 2017-11-21 Last updated: 2018-03-14Bibliographically approved
Goienetxea Uriarte, A., Urenda Moris, M., Ng, A. H. C. & Oscarsson, J. (2016). Lean, Simulation and Optimization: A Win-Win combination. In: L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti (Ed.), Proceedings of the 2015 Winter Simulation Conference: . Paper presented at 2015 Winter Simulation Conference, December 6-9, Huntington Beach, California, USA (pp. 2227-2238). Piscataway, New Jersey: IEEE Computer Society
Open this publication in new window or tab >>Lean, Simulation and Optimization: A Win-Win combination
2016 (English)In: Proceedings of the 2015 Winter Simulation Conference / [ed] L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, Piscataway, New Jersey: IEEE Computer Society, 2016, p. 2227-2238Conference paper, Published paper (Refereed)
Abstract [en]

Lean and simulation analysis are driven by the same objective, how to better design and improve processes making the companies more competitive. The adoption of lean has been widely spread in companies from public to private sectors and simulation is nowadays becoming more and more popular. Several authors have pointed out the benefits of combining simulation and lean, however, they are still rarely used together in practice. Optimization as an additional technique to this combination is even a more powerful approach especially when designing and improving complex processes with multiple conflicting objectives. This paper presents the mutual benefits that are gained when combining lean, simulation and optimization and how they overcome each other´s limitations. A framework including the three concepts, some of the barriers for its implementation and a real-world industrial example are also described.

Place, publisher, year, edition, pages
Piscataway, New Jersey: IEEE Computer Society, 2016
Series
Proceedings - Winter Simulation Conference, ISSN 0891-7736, E-ISSN 1558-4305 ; 2016
Keyword
Lean, Simulation, Optimization, Decision making
National Category
Production Engineering, Human Work Science and Ergonomics Computer Systems
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11766 (URN)10.1109/WSC.2015.7408335 (DOI)000399133902010 ()2-s2.0-84962838930 (Scopus ID)978-1-4673-9743-8 (ISBN)978-1-4673-9741-4 (ISBN)978-1-4673-9742-1 (ISBN)
Conference
2015 Winter Simulation Conference, December 6-9, Huntington Beach, California, USA
Projects
Simulation-based Multi-objective Optimization for Lean production and logistic Networks
Funder
Knowledge Foundation
Available from: 2015-12-17 Created: 2015-12-17 Last updated: 2018-03-29Bibliographically approved
Morshedzadeh, I., Oscarsson, J., Ng, A. H. .., Jeusfeld, M. A. & Jenefeldt, A. (2016). Real World Data Identification and Classification for Support of Virtual Confidence. In: : . Paper presented at 7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016.
Open this publication in new window or tab >>Real World Data Identification and Classification for Support of Virtual Confidence
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2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Shortening of the product development process time is one of the main approaches for all enterprises to offer their products to the market. Virtual manufacturing tools can help companies to reduce their time to market, by reduction of the engineering lead time. Extensive use of virtual engineering models results in a need for verification of the model’s accuracy. This virtual engineering usability and assessment have been named virtual confidence. The two main factors of the achievement of this confidence are the accuracy of the virtual models and the virtual engineering results.

For controlling of both above factors, a complete virtual model and related virtual model knowledge are needed. These knowledges can be tacit or explicit. For exploring explicit knowledge, a data and information collection from different disciplines in the organization is needed.

In this paper, a data map with focus on the manufacturing engineering scope will be presented. This data map is generated from different data sources at a manufacturing plant, and gives an overview of different data that exist at different data sources, in the area of manufacturing. Combining real world data from different sources with virtual engineering model data supports, amongst others, establishment of virtual confidence.

Keyword
Virtual confidence, manufacturing knowledge, data map, Product lifecycle management (PLM), explicit knowledge
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering; Information Systems
Identifiers
urn:nbn:se:his:diva-13736 (URN)
Conference
7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016
Projects
KDDS
Funder
Knowledge Foundation
Available from: 2017-06-16 Created: 2017-06-16 Last updated: 2017-11-27Bibliographically approved
Oscarsson, J., Jeusfeld, M. A. & Jenefeldt, A. (2016). Towards Virtual Confidence - Extended Product Lifecycle Management. In: Abdelaziz Bouras, Benoit Eynard, Sebti Foufou & Klaus-Dieter Thoben (Ed.), Product Lifecycle Management in the Era of Internet of Things: 12th IFIP WG 5.1 International Conference, PLM 2015, Doha, Qatar, October 19-21, 2015, Revised Selected Papers. Paper presented at 12th IFIP International Conference on Product Lifecycle Management (PLM 2015), Doha, Qatar, October 19-21, 2015 (pp. 708-717). Paper presented at 12th IFIP International Conference on Product Lifecycle Management (PLM 2015), Doha, Qatar, October 19-21, 2015. Springer, 467
Open this publication in new window or tab >>Towards Virtual Confidence - Extended Product Lifecycle Management
2016 (English)In: Product Lifecycle Management in the Era of Internet of Things: 12th IFIP WG 5.1 International Conference, PLM 2015, Doha, Qatar, October 19-21, 2015, Revised Selected Papers / [ed] Abdelaziz Bouras, Benoit Eynard, Sebti Foufou & Klaus-Dieter Thoben, Springer, 2016, Vol. 467, p. 708-717Chapter in book (Refereed)
Abstract [en]

Product lifecycle management (PLM) systems maintain amongst others the specifications and designs of product, process and resource artefacts and thus serve as the basis for realizing the concept of Virtual Manufacturing, and play a vital role in shortening the leadtimes for the engineering processes. Design of new products requires numerous experiments and test-runs of new facilities that delays the product release and causes high costs if performed in the real world. Virtualization promises to reduce these costs by simulating the reality. However, the results of the simulation must predict the real results to be useful. This is called virtual confidence. We propose a knowledge base approach to capture and maintain the virtual confidence in simulation results. To do so, the provenance of results of real, experimental and simulated processes are recorded and linked via confirmation objects.

Place, publisher, year, edition, pages
Springer, 2016
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 467
Keyword
Virtual confidence Simulation data management Provenance Ontology
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering; Information Systems
Identifiers
urn:nbn:se:his:diva-12163 (URN)10.1007/978-3-319-33111-9_64 (DOI)000378549400064 ()2-s2.0-84964842018 (Scopus ID)978-3-319-33110-2 (ISBN)978-3-319-33111-9 (ISBN)
Conference
12th IFIP International Conference on Product Lifecycle Management (PLM 2015), Doha, Qatar, October 19-21, 2015
Funder
Knowledge Foundation
Available from: 2016-04-22 Created: 2016-04-22 Last updated: 2018-03-28
De Vin, L., Oscarsson, J., Ng, A., Jägstam, M. & Karlsson, T. (2004). Manufacturing simulation: Good practice, pitfalls, and advanced applications. In: Phelan, P. (Ed.), The 21st International Manufacturing Conference: IMC. Paper presented at Publications | PAIPR group 21st International Manufacturing Conference, University of Limerick, Ireland, 1st – 3rd September 2004 (pp. 156-163).
Open this publication in new window or tab >>Manufacturing simulation: Good practice, pitfalls, and advanced applications
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2004 (English)In: The 21st International Manufacturing Conference: IMC / [ed] Phelan, P., 2004, p. 156-163Conference paper, Published paper (Refereed)
Abstract [en]

The paper describes manufacturing simulation with a focus on discrete event simulation and computer aided robotics. Some generic good practices, problems, and pitfalls in the use of simulation are described. Some advanced applications of manufacturing simulation are described and elucidated on the hand of a system for simulation-based service & maintenance. Simulation-based decision support and information fusion are closely related, and plans for novel synergistic research in these area are presented

Research subject
Technology
Identifiers
urn:nbn:se:his:diva-1515 (URN)1-8746537-7-1 (ISBN)
Conference
Publications | PAIPR group 21st International Manufacturing Conference, University of Limerick, Ireland, 1st – 3rd September 2004
Available from: 2007-07-06 Created: 2007-07-06 Last updated: 2017-12-20Bibliographically approved
De Vin, L., Ng, A. & Oskarsson, J. (2004). Simulation-based decision support for manufacturing system life cycle management. Journal of Advanced Manufacturing Systems, 3(2), 115-128
Open this publication in new window or tab >>Simulation-based decision support for manufacturing system life cycle management
2004 (English)In: Journal of Advanced Manufacturing Systems, ISSN 0219-6867, Vol. 3, no 2, p. 115-128Article in journal (Refereed) Published
Abstract [en]

Previous research has highlighted the role of virtual engineering tools in the development of manufacturing machinery systems. Simulation models created for this purpose can potentially be used to provide support for other tasks, such as operational planning and service and maintenance. This requires that the simulation models can be fed with historic data as well as with snapshot data. Furthermore, the models must be able to communicate with other business software. The paper describes how simulation models can be used for operational production planning and for service and maintenance support. Benefits include a better possibility to verify production plans and the possibility to monitor and service manufacturing machinery from remote locations. Furthermore, the expanded and continuously updated models provide a good tool to study the effect of, for instance, planned new product introduction in existing manufacturing systems. The paper also presents directions for future research. One ambition is to add AI tools to the system so as to develop a semi-autonomous system for decision support

Place, publisher, year, edition, pages
World Scientific, 2004
Keyword
Simulation, decision making, manufacturing
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-1553 (URN)10.1142/S0219686704000454 (DOI)2-s2.0-10644219580 (Scopus ID)
Available from: 2007-07-13 Created: 2007-07-13 Last updated: 2017-12-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9690-890X

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