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Lidberg, Simon, MSc.ORCID iD iconorcid.org/0000-0003-1215-152x
Publications (4 of 4) 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
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1215-152x

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