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Pehrsson, Leif
Publications (10 of 24) Show all publications
Pehrsson, L. & Karlsson, I. (2022). Optimisation with multi-objective rule extraction for manufacturing management. International Journal of Manufacturing Research, 17(4), 452-475
Open this publication in new window or tab >>Optimisation with multi-objective rule extraction for manufacturing management
2022 (English)In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 17, no 4, p. 452-475Article in journal (Refereed) Published
Abstract [en]

Industry is foreseeing rapid developments in the ability tocapture data within its manufacturing operations and the interest in methodsfor extracting knowledge from such data is increasing. Through digitalrepresentations of manufacturing operations, future scenarios can be modeledand developed with analysis tools based on simulation in combination withmulti-objective optimisation. The results from such analysis tools may bechallenging to interpret, especially when expanding the scope to searchingfor information patterns. An emerging multi-objective rule extraction method,with the ability to handle discrete input parameters, has been furtherdeveloped towards integration in an intelligent decision support system.

Place, publisher, year, edition, pages
InderScience Publishers, 2022
Keywords
simulation, optimisation, decision-support, data mining, rule extraction, manufacturing management
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-22120 (URN)10.1504/ijmr.2022.127107 (DOI)000889321900005 ()2-s2.0-85144414345 (Scopus ID)
Available from: 2022-12-08 Created: 2022-12-08 Last updated: 2023-01-17Bibliographically approved
Pehrsson, L., Aslam, T. & Frantzén, M. (2021). Aggregated models for decision-support in manufacturing systems management. International Journal of Manufacturing Research, 16(3), 217-240
Open this publication in new window or tab >>Aggregated models for decision-support in manufacturing systems management
2021 (English)In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 16, no 3, p. 217-240Article in journal (Refereed) Published
Abstract [en]

Many industrial challenges can be related to the setup of manufacturing plants and supply chains. While there are techniques available for discrete event simulation of production lines, the opportunities of applying such techniques on higher manufacturing network levels are not explored to the same extent. With established methods for optimisation of manufacturing lines showing proven potential in conceptual analysis and development of production lines, the application of such optimisation methods on higher level manufacturing networks is a subject for further exploration. In this paper, an extended aggregation technique for discrete event simulation of higher level manufacturing systems is discussed, proposed, tested, and verified with real-world problem statements as a proof of concept. The contribution of the new technique is to enable the application of DES models, with reasonable computational requirements, at higher level manufacturing networks. The proposed technique can be used to generate valuable decision information supporting conceptual systems development.

Place, publisher, year, edition, pages
InderScience Publishers, 2021
Keywords
aggregation, discrete event simulation, DES, optimisation, decision-support, manufacturing systems management
National Category
Computer Sciences
Research subject
VF-KDO; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-21820 (URN)10.1504/IJMR.2021.117926 (DOI)000849830400001 ()
Note

Pehrsson, Leif (corresponding author)

Available from: 2022-09-16 Created: 2022-09-16 Last updated: 2022-11-24Bibliographically 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., 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
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
Show others...
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: 2021-10-06
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, 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, 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)978-1-5386-3428-8 (ISBN)978-1-5386-3429-5 (ISBN)978-1-5386-3430-1 (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: 2020-09-23Bibliographically 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
Projects
Production of the next generation powertrains in Sweden [2017-01237_Vinnova]; University of Skövde
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