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  • 1.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    Karlsson, Thomas
    University of Skövde, School of Technology and Society.
    Virtual Manufacturing: Good Practice, Pitfalls and Advanced Applications2006In: SME Technical Paper, ISSN 0081-1653, p. TP06PUB41-Article in journal (Refereed)
    Abstract [en]

    This paper describes manufacturing (resource) 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. Various advanced applications of manufacturing simulation are described and elucidated on the hand of a system for simulation-based service and maintenance. The paper also describes briefly how simulation-based decision support and information fusion are related, and how this can result in synergistic research across these areas

  • 2.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Oscarsson, Jan
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    Karlsson, Thomas
    University of Skövde, School of Technology and Society.
    Manufacturing simulation: Good practice, pitfalls, and advanced applications2004In: The 21st International Manufacturing Conference: IMC / [ed] Phelan, P., 2004, p. 156-163Conference 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

  • 3.
    Karlsson, Thomas
    et al.
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    Effective manufacturing resource modelling2003In: the 20th International Manufacturing Conference, 2003, p. 422-429Conference paper (Refereed)
    Abstract [en]

    Information management in 21st century industry has many aspects that have to be fully understood and covered in an information plan. With the emergence of CAD, problems with version management became more profound. Product data management (PDM) systems offered a partial solution, but these systems do not cover complete product life cycle support across complete supply/demand chain networks and seldom address manufacturing resource and process modeling in an adequate manner. This paper discusses some trends and tools in manufacturing information management and product life cycle support, with a focus on reuse of existing production resource information throughout the product process resource domain

  • 4.
    Karlsson, Thomas
    et al.
    University of Skövde, School of Technology and Society.
    Oscarsson, Jan
    University of Skövde, School of Technology and Society.
    On Information Management for Advanced Virtual Manufacturing2005In: FAIM 2005: Volume I: Proceedings of the 15th International conference on flexible automation and intelligent manufacturing, July 18th-20th, 2005 / [ed] Esther Alvarez, Jalal Ashayeri, William G. Sullivan, Munir Ahmad, Bilbao: University of Deusto , 2005, p. 817-824Conference paper (Refereed)
  • 5.
    Karlsson, Thomas
    et al.
    University of Skövde, School of Technology and Society.
    Rogstrand, Victoria
    Kungliga tekniska högskolan (KTH), Skolan för industriell teknik och management (ITM), Industriell produktion, Datorsystem för konstruktion och tillverkning.
    De Vin, Leo J.
    University of Skövde, School of Technology and Society.
    Verifying manufacturing requirements using tools for digital plant technology2004Conference paper (Other academic)
  • 6.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Hemrik
    University of Skövde, School of Technology and Society.
    Lezama, Thomas
    University of Skövde, School of Technology and Society.
    Persson, Anna
    University of Skövde, School of Technology and Society.
    Andersson, Marcus
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    Web Services for Metamodel-Assisted Parallel Simulation Optimization2007In: IMECS 2007: International Multiconference of Engineers and Computer Scientists, Vols I and II, International Association of Engineers, 2007, p. 879-885Conference paper (Refereed)
    Abstract [en]

    This paper presents the OPTIMISE platform currently developed in the research project OPTIMIST. The aim of OPTIMISE is to facilitate research on metamodel-assisted simulation optimisation using soft computing techniques by providing a platform for the development and evaluation of new algorithms.

  • 7.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Lezama, Thomas
    University of Skövde, School of Technology and Society.
    Persson, Anna
    University of Skövde, School of Technology and Society.
    Andersson, Marcus
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    OPTIMISE: An Internet-Based Platform for Metamodel-Assisted Simulation Optimization2008In: Advances in Communication Systems and Electrical Engineering / [ed] Xu Huang, Yuh-Shyan Chen, Sio-Iong Ao, Springer Science+Business Media B.V., 2008, p. 281-296Chapter in book (Refereed)
    Abstract [en]

    Computer simulation has been described as the most effective tool for de-signing and analyzing systems in general and discrete-event systems (e.g., production or logistic systems) in particular (De Vin et al. 2004). Historically, the main disadvantage of simulation is that it was not a real optimization tool. Recently, research efforts have been focused on integrating metaheuristic algorithms, such as genetic algorithms (GA) with simulation software so that “optimal” or close to optimal solutions can be found automatically. An optimal solution here means the setting of a set of controllable design variables (also known as decision variables) that can minimize or maximize an objective function. This approach is called simulation optimization or simulation-based optimization (SBO), which is perhaps the most important new simulation technology in the last few years (Law and McComas 2002). In contrast to other optimization problems, it is assumed that the objective function in an SBO problem cannot be evaluated analytically but have to be estimated through deterministic/ stochastic simulation.

  • 8.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Lezama, Thomas
    University of Skövde, School of Technology and Society.
    Ekberg, Jonas
    Falk, Stephan
    Stablum, Peter
    Simulation-Based Multi-Objective Optimization of a Real-World Operation Scheduling Problem2006In: WSC '06 Proceedings of the 38th conference on Winter simulation, Winter Simulation Conference , 2006, p. 1757-1764Conference paper (Refereed)
    Abstract [en]

    This paper presents a successful application of simulation-based multi-objective optimization of a complex real-world scheduling problem. Concepts of the implemented simulation-based optimization architecture are described, as well as how different components of the architecture are implemented. Multiple objectives are handled in the optimization process by considering the decision makers' preferences using both prior and posterior articulations. The efficiency of the optimization process is enhanced by performing culling of solutions before using the simulation model, avoiding unpromising solutions to be unnecessarily processed by the computationally expensive simulation.

  • 9.
    Urenda Moris, Matias
    et al.
    University of Skövde, School of Technology and Society.
    Lezama, Thomas
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    Robust design of a Maternity ward supported by discrete event simulation: a case study2007In: Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2007, 2007, p. 107-114Conference paper (Refereed)
1 - 9 of 9
CiteExportLink to result list
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  • harvard1
  • ieee
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  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
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  • sv-SE
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  • text
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