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Pehrsson, Leif
Publications (10 of 21) Show all publications
Lidberg, S., Aslam, T., Pehrsson, L. & Ng, A. H. C. (2019). 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
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
2019 (English)In: Flexible Services and Manufacturing Journal, ISSN 1936-6582, E-ISSN 1936-6590Article in journal (Refereed) Epub ahead of print
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, 2019
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
Identifiers
urn:nbn:se:his:diva-17480 (URN)10.1007/s10696-019-09362-7 (DOI)2-s2.0-85068764729 (Scopus ID)
Available from: 2019-07-25 Created: 2019-07-25 Last updated: 2019-08-19Bibliographically 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
Available from: 2019-01-15 Created: 2019-01-15 Last updated: 2019-09-04Bibliographically 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: 2019-04-08Bibliographically approved
Aslam, T., Syberfeldt, A., Ng, A., Pehrsson, L. & Urenda-Moris, M. (2018). Towards an industrial testbed for holistic virtual production development. 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. 369-374). Amsterdam: IOS Press
Open this publication in new window or tab >>Towards an industrial testbed for holistic virtual production development
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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. 369-374Conference paper, Published paper (Refereed)
Abstract [en]

Virtual production development is adopted by many companies in the production industry and digital models and virtual tools are utilized for strategic, tactical and operational decisions in almost every stage of the value chain. This paper suggest a testbed concept that aims the production industry to adopt a virtual production development process with integrated tool chains that enables holistic optimizations, all the way from the overall supply chain performance down to individual equipment/devices. The testbed, which is fully virtual, provides a mean for development and testing of integrated digital models and virtual tools, including both technical and methodological aspects.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
Virtual production development, testbed, integrated tool chains, simulation, optimization
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16375 (URN)10.3233/978-1-61499-902-7-369 (DOI)000462212700059 ()2-s2.0-85057415907 (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
Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2019-04-08Bibliographically 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: 2019-06-18
Karlsson, I., Bernedixen, J., Ng, A. H. C. & Pehrsson, L. (2017). Combining augmented reality and simulation-based optimization for decision support in manufacturing. In: W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page (Ed.), Proceedings of the 2017 Winter Simulation Conference: . Paper presented at 2017 Winter Simulation Conference, WSC 2017, Las Vegas, USA, 3-6 December 2017 (pp. 3988-3999). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Combining augmented reality and simulation-based optimization for decision support in manufacturing
2017 (English)In: Proceedings of the 2017 Winter Simulation Conference / [ed] W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3988-3999Conference paper, Published paper (Refereed)
Abstract [en]

Although the idea of using Augmented Reality and simulation within manufacturing is not a new one, the improvement of hardware enhances the emergence of new areas. For manufacturing organizations, simulation is an important tool used to analyze and understand their manufacturing systems; however, simulation models can be complex. Nonetheless, using Augmented Reality to display the simulation results and analysis can increase the understanding of the model and the modeled system. This paper introduces a decision support system, IDSS-AR, which uses simulation and Augmented Reality to show a simulation model in 3D. The decision support system uses Microsoft HoloLens, which is a head-worn hardware for Augmented Reality. A prototype of IDSS-AR has been evaluated with a simulation model depicting a real manufacturing system on which a bottleneck detection method has been applied. The bottleneck information is shown on the simulation model, increasing the possibility of realizing interactions between the bottlenecks. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Winter Simulation Conference. Proceedings, ISSN 0891-7736, E-ISSN 1558-4305
Keywords
Artificial intelligence, Augmented reality, Automobile drivers, Decision support systems, Hardware, Optimization, Bottleneck detection, Decision supports, Manufacturing IS, Manufacturing organizations, MicroSoft, Simulation model, Simulation-based optimizations, Quality control
National Category
Computer Sciences Computer Systems Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-15109 (URN)10.1109/WSC.2017.8248108 (DOI)000427768604018 ()2-s2.0-85044511682 (Scopus ID)9781538634288 (ISBN)9781538634301 (ISBN)
Conference
2017 Winter Simulation Conference, WSC 2017, Las Vegas, USA, 3-6 December 2017
Available from: 2018-04-30 Created: 2018-04-30 Last updated: 2019-01-24Bibliographically approved
Pehrsson, L., Ng, A. H. C. & Bernedixen, J. (2016). Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis. Journal of manufacturing systems, 39, 24-37
Open this publication in new window or tab >>Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis
2016 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, p. 24-37Article in journal (Refereed) Published
Abstract [en]

Manufacturing companies are operating in a severely competitive global market, which renders an urgent need for them to explore new methods to enhance the performance of their production systems in order to retain their competitiveness. Regarding the performance of a production system, it is not sufficient simply to detect which operations to improve, but it is imperative to pinpoint the right actions in the right order to avoid sub-optimizations and wastes in time and expense. Therefore, a more accurate and efficient method for supporting system improvement decisions is greatly needed in manufacturing systems management. Based on research in combining simulation-based multi-objective optimization and post-optimality analysis methods for production systems design and analysis, a novel method for the automatic identification of bottlenecks and improvement actions, so-called Simulation-based Constraint Identification (SCI), is proposed in this paper. The essence of the SCI method is the application of simulation-based multi-objective optimization with the conflicting objectives to maximize the throughput and minimize the number of required improvement actions simultaneously. By using post-optimality analysis to process the generated optimization dataset, the exact improvement actions needed to attain a certain level of performance of the production line are automatically put into a rank order. In other words, when compared to other existing approaches in bottleneck detection, the key novelty of combining multi-objective optimization and post-optimality analysis is to make SCI capable of accurately identifying a rank order for the required levels of improvement for a large number of system parameters which impede the performance of the entire system, in a single optimization run. At the same time, since SCI is basically built a top a simulation-based optimization approach, it is capable of handling large-scale, real-world system models with complicated process characteristics. Apart from introducing such a method, this paper provides some detailed validation results from applying SCI both in hypothetical examples that can easily be replicated as well as a complex, real-world industrial improvement project. The promising results compared to other existing bottleneck detection methods have demonstrated that SCI can provide valuable higher-level information to support confident decision-making in production systems improvement.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Multi-objective optimization, Simulation, Production system, SCI
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12020 (URN)10.1016/j.jmsy.2016.02.001 (DOI)000376694200003 ()2-s2.0-84959481904 (Scopus ID)
Available from: 2016-03-08 Created: 2016-03-08 Last updated: 2019-02-25Bibliographically approved
Pehrsson, L., Karlsson, I. & Ng, A. H. C. (2016). Towards Automated Multi-Objective Rule Extraction. In: José Évora-Gómez & José Juan Hernandéz-Cabrera (Ed.), Proceedings of the 2016 European Simulation and Modelling Conference: . Paper presented at The 2016 European Simulation and Modelling Conference, 30th ESM 2016, Las Palmas, Spain, 26 October 26-28, 2016 (pp. 64-68). EUROSIS - The European Multidisciplinary Society for Modelling and Simulation Technology
Open this publication in new window or tab >>Towards Automated Multi-Objective Rule Extraction
2016 (English)In: Proceedings of the 2016 European Simulation and Modelling Conference / [ed] José Évora-Gómez & José Juan Hernandéz-Cabrera, EUROSIS - The European Multidisciplinary Society for Modelling and Simulation Technology , 2016, p. 64-68Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
EUROSIS - The European Multidisciplinary Society for Modelling and Simulation Technology, 2016
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14221 (URN)2-s2.0-85016009857 (Scopus ID)978-90-77381-95-3 (ISBN)
Conference
The 2016 European Simulation and Modelling Conference, 30th ESM 2016, Las Palmas, Spain, 26 October 26-28, 2016
Available from: 2017-10-10 Created: 2017-10-10 Last updated: 2018-02-01Bibliographically approved
Pehrsson, L., Frantzén, M., Aslam, T. & Ng, A. H. C. (2015). Aggregated line modeling for simulation and optimization of manufacturing systems. 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 WSC '15 Winter Simulation Conference, Huntington Beach, CA, USA — December 06 - 09, 2015 (pp. 3632-3643). Piscataway, NJ, USA: IEEE Press
Open this publication in new window or tab >>Aggregated line modeling for simulation and optimization of manufacturing systems
2015 (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, NJ, USA: IEEE Press, 2015, p. 3632-3643Conference paper, Published paper (Refereed)
Abstract [en]

In conceptual analysis of higher level manufacturing systems, for instance, when the constraint on system level is sought, it may not be very practical to use detailed simulation models. Developing detailed models on supply chain level or plant wide level may be very time consuming and might also be computationally costly to execute, especially if optimization techniques are to be applied. Aggregation techniques, simplifying a detailed system into fewer objects, can be an effective method to reduce the required computational resources and to shorten the development time. An aggregated model can be used to identify the main system constraints, dimensioning inter-line buffers, and focus development activities on the critical issues from a system performance perspective. In this paper a novel line aggregation technique suitable for manufacturing systems optimization is proposed, analyzed and tested in order to establish a proof of concept while demonstrating the potential of the technique.

Place, publisher, year, edition, pages
Piscataway, NJ, USA: IEEE Press, 2015
Series
Winter Simulation Conference. Proceedings, ISSN 0891-7736
Keywords
Simulation, Modeling, Manufacturing, Optimization
National Category
Other Mechanical Engineering
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11899 (URN)10.1109/WSC.2015.7408522 (DOI)000399133903069 ()2-s2.0-84962829106 (Scopus ID)978-1-4673-9743-8 (ISBN)
Conference
WSC '15 Winter Simulation Conference, Huntington Beach, CA, USA — December 06 - 09, 2015
Available from: 2016-02-12 Created: 2016-02-11 Last updated: 2018-05-07Bibliographically approved
Bernedixen, J., Ng, A. H. C., Pehrsson, L. & Antonsson, T. (2015). Simulation-based multi-objective bottleneck improvement: Towards an automated toolset for industry. 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 WSC '15 Winter Simulation Conference, Huntington Beach, CA, USA — December 06 - 09, 2015 (pp. 2183-2194). Press Piscataway, NJ: IEEE Press
Open this publication in new window or tab >>Simulation-based multi-objective bottleneck improvement: Towards an automated toolset for industry
2015 (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, Press Piscataway, NJ: IEEE Press, 2015, p. 2183-2194Conference paper, Published paper (Refereed)
Abstract [en]

Manufacturing companies of today are under pressure to run their production most efficiently in order to sustain their competitiveness. Manufacturing systems usually have bottlenecks that impede their performance, and finding the causes of these constraints, or even identifying their locations, is not a straightforward task. SCORE (Simulation-based COnstraint REmoval) is a promising method for detecting and ranking bottlenecks of production systems, that utilizes simulation-based multi-objective optimization (SMO). However, formulating a real-world, large-scale industrial bottleneck analysis problem into a SMO problem using the SCORE-method manually include tedious and error-prone tasks that may prohibit manufacturing companies to benefit from it. This paper presents how the greater part of the manual tasks can be automated by introducing a new, generic way of defining improvements of production systems and illustrates how the simplified application of SCORE can assist manufacturing companies in identifying their production constraints.

Place, publisher, year, edition, pages
Press Piscataway, NJ: IEEE Press, 2015
Series
Winter Simulation Conference. Proceedings, ISSN 0891-7736
Keywords
Simulation, Optimization, Manufacturing
National Category
Other Mechanical Engineering
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11919 (URN)10.1109/WSC.2015.7408331 (DOI)000399133902006 ()2-s2.0-84962811728 (Scopus ID)978-1-4673-9743-8 (ISBN)
Conference
WSC '15 Winter Simulation Conference, Huntington Beach, CA, USA — December 06 - 09, 2015
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2018-05-31
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