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Publications (10 of 174) Show all publications
Amouzgar, K., Bandaru, S., Andersson, T. J. & Ng, A. H. C. (2018). A framework for simulation based multi-objective optimization and knowledge discovery of machining process. The International Journal of Advanced Manufacturing Technology, 98(9-12), 2469-2486
Open this publication in new window or tab >>A framework for simulation based multi-objective optimization and knowledge discovery of machining process
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, no 9-12, p. 2469-2486Article in journal (Refereed) Published
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:his:diva-15136 (URN)10.1007/s00170-018-2360-8 (DOI)000444704300020 ()2-s2.0-85049664435 (Scopus ID)
Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-10-01
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance. European Journal of Operational Research
Open this publication in new window or tab >>A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance
2018 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860Article in journal (Refereed) Submitted
National Category
Production Engineering, Human Work Science and Ergonomics Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15064 (URN)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-10-04
Senington, R., Baumeister, F., Ng, A. & Oscarsson, J. (2018). A linked data approach for the connection of manufacturing processes with production simulation models. Paper presented at 28th CIRP Design Conference, Nantes, France, May 23-25, 2018. Procedia CIRP, 70, 440-445
Open this publication in new window or tab >>A linked data approach for the connection of manufacturing processes with production simulation models
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 70, p. 440-445Article in journal (Refereed) Published
Abstract [en]

This paper discusses the expected benefits of using linked data for the tasks of gathering, managing and understanding the data of smart factories. It has the further specific focus of using this data to maintaining a Digital Twin for the purposes of analysis and optimisation of such factories. The proposals are motivated by the use of an industrial example looking at the types of information required, the variation in data which is available and the requirements of an analysis platform to provide parameters for seamless, automated simulation and optimisation. 

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Smart Factory, Digital Twin, Linked Data
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16005 (URN)10.1016/j.procir.2018.03.243 (DOI)000437126800074 ()2-s2.0-85051264447 (Scopus ID)
Conference
28th CIRP Design Conference, Nantes, France, May 23-25, 2018
Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2018-10-02
Bandaru, S. & Ng, A. H. C. (2018). An empirical comparison of metamodeling strategies in noisy environments. In: Hernan Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018): . Paper presented at Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018 (pp. 817-824). New York, NY, USA: ACM Digital Library, Article ID 3205509.
Open this publication in new window or tab >>An empirical comparison of metamodeling strategies in noisy environments
2018 (English)In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018) / [ed] Hernan Aguirre, New York, NY, USA: ACM Digital Library, 2018, p. 817-824, article id 3205509Conference paper, Published paper (Refereed)
Abstract [en]

Metamodeling plays an important role in simulation-based optimization by providing computationally inexpensive approximations for the objective and constraint functions. Additionally metamodeling can also serve to filter noise, which is inherent in many simulation problems causing optimization algorithms to be mislead. In this paper, we conduct a thorough statistical comparison of four popular metamodeling methods with respect to their approximation accuracy at various levels of noise. We use six scalable benchmark problems from the optimization literature as our test suite. The problems have been chosen to represent different types of fitness landscapes, namely, bowl-shaped, valley-shaped, steep ridges and multi-modal, all of which can significantly influence the impact of noise. Each metamodeling technique is used in combination with four different noise handling techniques that are commonly employed by practitioners in the field of simulation-based optimization. The goal is to identify the metamodeling strategy, i.e. a combination of metamodeling and noise handling, that performs significantly better than others on the fitness landscapes under consideration. We also demonstrate how these results carry over to a simulation-based optimization problem concerning a scalable discrete event model of a simple but realistic production line.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2018
Series
GECCO '18
Keywords
simulation, optimization, metamodeling, noise
National Category
Computer Sciences Other Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15966 (URN)10.1145/3205455.3205509 (DOI)978-1-4503-5618-3 (ISBN)
Conference
Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018
Projects
Synergy KDDS
Funder
Knowledge Foundation, 41231
Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2018-10-17Bibliographically approved
Fathi, M., Nourmohammadi, A. & Ng, A. H. C. (2018). Assembly Line Balancing Type-E with Technological Requirement: A Mathematical Model. In: Peter Thorvald, Keith Case (Ed.), 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. 183-188). Amsterdam: IOS Press
Open this publication in new window or tab >>Assembly Line Balancing Type-E with Technological Requirement: A Mathematical 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. 183-188Conference paper, Published paper (Refereed)
Abstract [en]

This study is motivated by a real-world assembly line in an automotive manufacturing company and it addresses the simple assembly line balancing problem type-E (SALBPE). The SALBPE aims to maximize the balance efficiency (BE) through determining the best combinations of cycle time and station number. To cope with the problem, a mixed integer nonlinear programming (MINLP) model is proposed. The MINLP model differs from the existing ALBPE models as it includes the technological requirements of assembly tasks and optimizes the variation of workload beside the BE. The validity of the proposed model is tested by solving the real-world case study and a set of benchmark problems.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
line balancing, type E, technological requirement, mathematical model
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16203 (URN)10.3233/978-1-61499-902-7-183 (DOI)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: 2018-09-13 Created: 2018-09-13 Last updated: 2018-09-24Bibliographically approved
Ayani, M., Ganebäck, M. & Ng, A. H. C. (2018). Digital Twin: Applying emulation for machine reconditioning. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 243-248
Open this publication in new window or tab >>Digital Twin: Applying emulation for machine reconditioning
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 243-248Article in journal (Refereed) Published
Abstract [en]

Old machine reconditioning projects extend the life length of machines with reduced investments, however they frequently involve complex challenges. Due to the lack of technical documentation and the fact that the machines are running in production, they can require a reverse engineering phase and extremely short commissioning times. Recently, emulation software has become a key tool to create Digital Twins and carry out virtual commissioning of new manufacturing systems, reducing the commissioning time and increasing its final quality. This paper presents an industrial application study in which an emulation model is used to support a reconditioning project and where the benefits gained in the working process are highlighted.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
digital twin, emulation, virtual commissioning, industry 4.0, reconditioning, retrofitting
National Category
Control Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16078 (URN)10.1016/j.procir.2018.03.139 (DOI)2-s2.0-85049565560 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Projects
Twin
Available from: 2018-08-24 Created: 2018-08-24 Last updated: 2018-10-01Bibliographically approved
Morshedzadeh, I., Oscarsson, J., Ng, A. H. C., Aslam, T. & Frantzén, M. (2018). Multi-level management of discrete event simulation models in a product lifecycle management framework. Paper presented at 8th Swedish Production Symposium, SPS 2018, Stockholm, Sweden, May 16-18, 2018. Procedia Manufacturing, 25, 74-81
Open this publication in new window or tab >>Multi-level management of discrete event simulation models in a product lifecycle management framework
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2018 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 74-81Article in journal (Refereed) Published
Abstract [en]

Discrete event simulation (DES) models imitates the behavior of a production system. Models can be developed to reflect different levels of the production system, e.g supply chain level or manufacturing line level. Product Lifecycle Management (PLM) systems have been developed in order to manage product and manufacturing related data. DES models is one kind of product lifecycle’s data which can be managed by a PLM system. This paper presents a method and its implementation for management of interacting multi-level models utilizing a PLM system.

Keywords
Discrete event simulation, Product lifecycle management, Multi-level simulation
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16074 (URN)10.1016/j.promfg.2018.06.059 (DOI)
Conference
8th Swedish Production Symposium, SPS 2018, Stockholm, Sweden, May 16-18, 2018
Available from: 2018-08-24 Created: 2018-08-24 Last updated: 2018-09-28Bibliographically approved
Ayani, M., Ng, A. H. C. & Birtic, M. (2018). Optimizing Cycle Time and Energy Efficiency of a Robotic Cell sing an Emulation Model. In: Peter Thorvald, Keith Case (Ed.), Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd NationalConference on Manufacturing Research, September 11–13, 2018,University of Skövde, Sweden. Paper presented at 16th International Conference on Manufacturing Research (ICMR), incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden (pp. 411-416). Amsterdam: IOS Press, 8
Open this publication in new window or tab >>Optimizing Cycle Time and Energy Efficiency of a Robotic Cell sing an Emulation Model
2018 (English)In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd NationalConference on Manufacturing Research, September 11–13, 2018,University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, Vol. 8, p. 411-416Conference paper, Published paper (Refereed)
Abstract [en]

Industrial automated systems are mostly designed and pre-adjusted to always work at their maximum production rate. This leaves room for important energy consumption reductions considering the production rate variations of factories in reality. This article presents a multi-objective optimization application targeting cycle time and energy consumption of a robotic cell. A novel approach is presented where an existing emulation model of a fictitious robotic cell was extended with low-level electrical components modeled and encapsulated as FMUs. The model, commanded by PLC and Robot Control software, was subjected to a multi-objective optimization algorithm in order to find the Pareto front between energy consumption and production rate. The result of the optimization process allows selecting the most efficient energy consumption for the robotic cell in order to achieve the required cycle.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
Emulation, Optimization, Energy efficiency, Robotic cell
National Category
Control Engineering Computer Systems
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16286 (URN)10.3233/978-1-61499-902-7-411 (DOI)978-1-61499-901-0 (ISBN)978-1-61499-902-7 (ISBN)
Conference
16th International Conference on Manufacturing Research (ICMR), incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden
Available from: 2018-10-09 Created: 2018-10-09 Last updated: 2018-10-12
Morshedzadeh, I., Oscarsson, J., Ng, A. H. C., Jeusfeld, M. A. & Sillanpaa, J. (2018). Product lifecycle management with provenance management and virtual models: an industrial use-case study. Paper presented at 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), 16-18 May 2018, Stockholm, Sweden. Procedia CIRP, 1190-1195
Open this publication in new window or tab >>Product lifecycle management with provenance management and virtual models: an industrial use-case study
Show others...
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, , p. 6p. 1190-1195Article in journal (Refereed) Published
Abstract [en]

Saving and managing virtual models’ provenance information (models’ history) can increase the level of reusability of those models. This paper describes a provenance management system (PMS) that has been developed based on an industrial case study.

The product lifecycle management (PLM) system, as a main data management system, is responsible for receiving virtual models and their related data from Computer-Aided technologies (CAx) and providing this information for the PMS. In this paper, the management of discrete event simulation data with the PLM system will be demonstrated as the first link of provenance data management chain (CAx-PLM-PMS).

Place, publisher, year, edition, pages
Elsevier, 2018. p. 6
Keywords
Discrete event simulation, Provenance, Product lifecycle
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering; Information Systems
Identifiers
urn:nbn:se:his:diva-15920 (URN)10.1016/j.procir.2018.03.157 (DOI)2-s2.0-85049581305 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), 16-18 May 2018, Stockholm, Sweden
Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2018-10-01Bibliographically approved
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation. Journal of Simulation, 12(2), 171-189
Open this publication in new window or tab >>Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation
2018 (English)In: Journal of Simulation, ISSN 1747-7778, E-ISSN 1747-7786, Vol. 12, no 2, p. 171-189Article in journal (Refereed) Published
National Category
Reliability and Maintenance Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15063 (URN)10.1080/17477778.2018.1467849 (DOI)000432552700008 ()2-s2.0-85047239919 (Scopus ID)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-10-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0111-1776

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