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  • 101.
    Lidberg, Simon
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Using Aggregated Discrete Event Simulation Models and Multi-Objective Optimization to Improve Real-World Factories2018In: 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 (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.

  • 102.
    Linnéusson, Gary
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Investigating Maintenance Performance: A Simulation Study2016In: Proceedings of the 7th Swedish Production Symposium, 2016Conference paper (Refereed)
    Abstract [en]

    Maintenance can be performed in multiple procedures, and it is hard to justify investments in preventive work. It is a complex equation between the inherent complexity of maintenance and its tight dependencies with production, but also the aspect of direct cost and consequential costs from activities. A model is presented that quantify dynamics of maintenance performance in order to enable a systems analysis on the total of consequences from different strategies. Simulation offers experimenting and learning on how performance is generated. The model is based on parts of previous research on maintenance modelling, system dynamics, maintenance theory, and mapping of practical information flows in maintenance. Two experiments are presented that both take off from a reactive strategy of maintenance performance, and implement two different strategies for preventive maintenance. Using the model enriches the analysis on how the aspects of maintenance performance work together with different maintenance strategies.

  • 103.
    Linnéusson, Gary
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance2019In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860Article in journal (Refereed)
    Abstract [en]

    Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research.

    Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement.

    Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.

  • 104.
    Linnéusson, Gary
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Justifying Maintenance Studying System Behavior: A Multipurpose Approach Using Multi-objective Optimization2017In: 35th International Conference of the System Dynamics Society 2017: Cambridge, Massachusetts, USA 16 - 20 July 2017 / [ed] J. Sterman, N. Repenning, Curran Associates, Inc., 2017, Vol. 2, p. 1061-1081Conference paper (Refereed)
    Abstract [en]

    Industrial maintenance includes rich internaldynamic complexity on how to deliver value. While the technical development hasprovided with applicable solutions in terms of reliability and condition basedmonitoring, managing maintenance is still an act of balancing, trying to pleasethe short-termism from the economic requirements and simultaneously address thenecessity of strategic and long-term thinking. By presenting an analysis tojustify maintenance studying system behavior, this paper exemplifies thecontribution of the combined approach of a system dynamics maintenanceperformance model and multi-objective optimization. The paper reveals howinsights from the investigation, of the near optimal Pareto-front solutions inthe objective space, can be drawn using visualization of performance ofselected parameters. According to our analysis, there is no return back to thesingle use of system dynamics; the contribution to the analysis of exploringsystem behavior, from applying multi-objective optimization, is extensive.However, for the practical application, the combined approach is not areplacement – but a complement. Where the interpretation of the visualizedPareto-fronts strongly benefits from the understanding of the model dynamics, inwhich important nonlinearities and delays can be revealed, and thus facilitateon the selected strategical path for implementation.

  • 105.
    Linnéusson, Gary
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Jönköping University, School of Engineering, Sweden.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation2018In: Journal of Simulation, ISSN 1747-7778, E-ISSN 1747-7786, Vol. 12, no 2, p. 171-189Article in journal (Refereed)
  • 106.
    Linnéusson, Gary
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Jönköping University, Sweden.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Relating strategic time horizons and proactiveness in equipment maintenance: a simulation-based optimization study2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1293-1298Article in journal (Refereed)
    Abstract [en]

    Identifying sustainable strategies to develop maintenance performance within the short-termism framework is indeed challenging. It requires reinforcing long-term capabilities while managing short-term requirements. This study explores differently applied time horizons when optimizing the tradeoff between conflicting objectives, in maintenance performance, which are: maximize availability, minimize maintenance costs, and minimize maintenance consequence costs. The study has applied multi-objective optimization on a maintenance performance system dynamics model that contains feedback structures that explains reactive and proactive maintenance behavior on a general level. The quantified results provide insights on how different time frames are conditional to enable more or less proactive maintenance behavior in servicing production.

  • 107.
    Linnéusson, Gary
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Towards strategic development of maintenance and its effects on production performance by using system dynamics in the automotive industry2018In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 200, p. 151-169Article in journal (Refereed)
    Abstract [en]

    Managing maintenance within an economical short-termism framework, without considering the consequential long-term cost effect, is very common in industry. This research presents a novel conceptual system dynamics model for the study of the dynamic behaviors of maintenance performance and costs, which aims to illuminate insights for the support of the long-term, strategic development of manufacturing maintenance. By novel, we claim the model promotes a system's view of maintenance costs that include its dynamic consequential costs as the combined result of several interacting maintenance levels throughout the constituent feedback structures. These range from the applied combination of maintenance methodologies to the resulting proactiveness in production, which is based on the rate of continuous improvements arising from the root cause analyses of breakdowns. The purpose of using system dynamics is to support the investigations of the causal relationships between strategic initiatives and performance results, and to enable analyses that take into consideration the time delays between different actions, in order to support the sound formulation of policies to develop maintenance and production performances. The model construction and validation process has been supported by two large maintenance organizations operating in the Swedish automotive industry. Experimental results show that intended changes can have both short and long-term consequences, and that obvious and hidden dynamic behavioral effects, which have not been reported in the literature previously, may be in the system. We believe the model can help to illuminate the holistic value of maintenance on the one hand and support its strategic development as well as the organizational transformation into proactiveness on the other.

  • 108.
    Moore, P. R.
    et al.
    De Montfort Univ, Mechatron Res Grp, Leicester LE1 9BH, Leics, England.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Yeo, S. H.
    De Montfort Univ, Mechatron Res Grp, Leicester LE1 9BH, Leics, England.
    Sundberg, Martin
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wong, C. B.
    De Montfort Univ, Mechatron Res Grp, Leicester LE1 9BH, Leics, England.
    De Vin, Leo
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Advanced machine service support using Internet-enabled three-dimensional-based virtual engineering2008In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 46, no 15, p. 4215-4235Article in journal (Refereed)
    Abstract [en]

    In the era of globalization, one of the key factors for manufacturing machine builders/suppliers to remain competitive is their capability to provide cost-effective and comprehensive machine service and maintenance for their clients at anytime, anywhere. Previous research has highlighted the role of virtual engineering tools in the design and development life cycle of manufacturing machinery systems. Virtual engineering models created during the development phase can potentially be used to provide valuable functions for many other tasks during the operational phase, including service and maintenance support. This paper introduces an innovative Internet-enabled three-dimensional-based virtual engineering framework that can be used for such purposes. Specifically, it addresses a system architecture that is designed to facilitate the tight integration between virtual engineering tools and a set of Internet-based reconfigurable modular maintenance supporting tools. This system architecture has been verified by implementations using different toolsets atop of various Internet technologies (e.g. XML Web services and LabView's Datasocket). Implementation details and successful industrial-based test cases are also provided in this paper.

  • 109.
    Moore, P. R.
    et al.
    Mechatronics Research Group, Faculty of Computing Sciences and Engineering, School of Engineering & Manufacture, De Montfort University, Leicester, UK.
    Pu, J.
    Mechatronics Research Group, Faculty of Computing Sciences and Engineering, School of Engineering & Manufacture, De Montfort University, Leicester, UK.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Wong, C. B.
    Mechatronics Research Group, Faculty of Computing Sciences and Engineering, School of Engineering & Manufacture, De Montfort University, Leicester, UK.
    Chong, S. K.
    Mechatronics Research Group, Faculty of Computing Sciences and Engineering, School of Engineering & Manufacture, De Montfort University, Leicester, UK.
    Chen, X.
    Mechatronics Research Group, Faculty of Computing Sciences and Engineering, School of Engineering & Manufacture, De Montfort University, Leicester, UK.
    Adolfsson, Josef
    University of Skövde, School of Technology and Society.
    Olofsgård, P.
    University of Skövde, School of Technology and Society.
    Lundgren, J. -O.
    Euromation AB, Skövde, Sweden.
    Virtual engineering: an integrated approach to agile manufacturing machinery design and control2003In: Mechatronics (Oxford), ISSN 0957-4158, E-ISSN 1873-4006, Vol. 13, no 10, p. 1105-1121Article in journal (Refereed)
    Abstract [en]

    A virtual manufacturing approach for designing, programming, testing, verifying and deploying control systems for agile modular manufacturing machinery are proposed in this paper. It introduces the concepts, operations, mechanisms and implementation techniques for integrating simulation environments and distributed control system environments so that the control logic programs that have been programmed and verified in the virtual environment can be seamlessly transferred to the distributed control system environment for controlling the real devices. The approach looks to exploit simulation in a much wider range of applications with great advantages in the design and development of manufacturing machine systems. In particular, it facilitates the verification of the runtime support applications using the simulation model before they are applied to the real system. Mechanisms that allow runtime data to be collected during operation of the real machinery to calibrate the simulation models are also proposed. The system implemented delivers a powerful set of software tools for realising agile modular manufacturing systems.

  • 110.
    Morshedzadeh, Iman
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Amouzgar, Kaveh
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Management of virtual models with provenance information in the context of product lifecycle management: industrial case studies2019In: Product Lifecycle Management (Volume 4): The Case Studies / [ed] John Stark, Cham: Springer, 2019, 1, p. 153-170Chapter in book (Refereed)
    Abstract [en]

    Using virtual models instead of physical models can help industries reduce the time and cost of developments, despite the time consuming process of building virtual models. Therefore, reusing previously built virtual models instead of starting from scratch can eliminate a large amount of work from users. Is having a virtual model enough to reuse it in another study or task? In most cases, not. Information about the history of that model makes it clear for the users to decide if they can reuse this model or to what extent the model is needed to be modified. A provenance management system (PMS) has been designed to manage provenance information, and it has been used with product lifecycle management system (PLM) and computer-aided technologies (CAx) to save and present historical information about a virtual model. This chapter presents a sequence-based framework of the CAx-PLM-PMS chain and two application case studies considering the implementation of this framework.

  • 111.
    Morshedzadeh, Iman
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Oscarsson, Jan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Frantzén, Marcus
    Volvo Car Corporation, Skövde, Sweden.
    Multi-level management of discrete event simulation models in a product lifecycle management framework2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 74-81Article in journal (Refereed)
    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.

  • 112.
    Morshedzadeh, Iman
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Oscarsson, Jan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Jeusfeld, Manfred A.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Sillanpaa, Janne
    InterSystems Sweden AB, Stockholm.
    Product lifecycle management with provenance management and virtual models: an industrial use-case study2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 6p. 1190-1195Article in journal (Refereed)
    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).

  • 113.
    Morshedzadeh, Iman
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Oscarsson, Jan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H.C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Jeusfeld, Manfred A.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Jenefeldt, Anders
    Volvo Cars Corporation, Skövde, Sweden.
    Real World Data Identification and Classification for Support of Virtual Confidence2016Conference paper (Refereed)
    Abstract [en]

    Shortening of the product development process time is one of the main approaches for all enterprises to offer their products to the market. Virtual manufacturing tools can help companies to reduce their time to market, by reduction of the engineering lead time. Extensive use of virtual engineering models results in a need for verification of the model’s accuracy. This virtual engineering usability and assessment have been named virtual confidence. The two main factors of the achievement of this confidence are the accuracy of the virtual models and the virtual engineering results.

    For controlling of both above factors, a complete virtual model and related virtual model knowledge are needed. These knowledges can be tacit or explicit. For exploring explicit knowledge, a data and information collection from different disciplines in the organization is needed.

    In this paper, a data map with focus on the manufacturing engineering scope will be presented. This data map is generated from different data sources at a manufacturing plant, and gives an overview of different data that exist at different data sources, in the area of manufacturing. Combining real world data from different sources with virtual engineering model data supports, amongst others, establishment of virtual confidence.

  • 114.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society.
    Adolfsson, Josef
    University of Skövde, School of Technology and Society.
    Sundberg, Martin
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    Virtual manufacturing for press line monitoring and diagnostics2008In: International journal of machine tools & manufacture, ISSN 0890-6955, E-ISSN 1879-2170, Vol. 48, no 5, p. 565-575Article in journal (Refereed)
    Abstract [en]

    Virtual manufacturing, or manufacturing simulation, can be divided into three domains: product domain, process domain, and resource domain. This paper focuses on the use of virtual manufacturing for press line monitoring and diagnostics. It contains an introduction to virtual manufacturing, divided into a general part on modelling and simulation, verification, validation and acceptance, and the division into the three domains, as well as a specific part on the use of simulation in these domains for sheet metal products, processes and processing equipment. The main software tools discussed in this paper are discrete event simulation and computer-aided robotics; the designed and implemented machine service support system is an example of advanced use of three-dimensional (3-D) graphical simulation in the resource domain. This system offers remote on-line monitoring and diagnostics functions as well as media player-type functions such as replay that allow a service and maintenance expert to analyse disturbances that occur at remote locations. A key feature of the system is 3-D graphical simulation with I/O synchronisation. This type of system is particularly useful for system integrators and machine builders that install press lines and press cells world-wide and need to guarantee a high level of availability of the installed machinery.

  • 115.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matias
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Jägstam, Mats
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Factory flow design and analysis using internet-enabled simulation-based optimization and automatic model generation2011In: Proceedings of the 2011 Winter Simulation Conference / [ed] S. Jain, R. Creasey & J. Himmelspach, IEEE conference proceedings, 2011, p. 2176-2188Conference paper (Refereed)
    Abstract [en]

    Despite simulation offers tremendous promise for designing and analyzing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conceptual phase of factory flow design. If simulation is used today for system design, it is more often used in later phases when important design decisions have already been made and costs are locked. With an aim to advocate the use of simulation in early phases of factory design and analysis, this paper introduces FACTS Analyzer, a toolset developed based on the concept of integrating model abstraction, automatic model generation and simulation-based optimization under an innovative Internet-based platform. Specifically, it addresses a novel model aggregation and generation method, which when combined together with other system components, like optimization engines, can synthetically enable simulation to become much easier to use and speed up the time-consuming model building, experimentation and optimization processes, in order to support optimal decision making.

  • 116.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    Sundberg, Martin
    University of Skövde.
    Oldefors, Fredrik
    University of Skövde.
    Moore, Philip
    De Montfort University, United Kingdom.
    Yeo, Sanho
    De Montfort University, United Kingdom.
    An integrated environment for machine system simulation, remote monitoring and fault detection2004Conference paper (Other academic)
  • 117.
    Ng, Amos
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Deb, Kalyanmoy
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Dudas, Catarina
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Simulation-based Innovization for production systems improvement: An industrial case study2009In: The International 3rd Swedish Production Symposium / [ed] B.-G. Rosén, The Swedish Production Academy , 2009, p. 278-286Conference paper (Refereed)
    Abstract [en]

    This paper introduces a new methodology for the design, analysis, and optimization of production systems. The methodology is based on the Innovization procedure originally introduced for unveiling new and innovative design principles in engineering design problems. Although the Innovization method is based on multi-objective optimization and post-optimality analyses of optimized solutions, it extends the scope beyond an optimization task and attempts to discover new design/operational rules/principles related to decision variables and objectives, in order to enable a deeper understanding of the problem. By integrating the concept of Innovization with discrete-event simulation, a new set of powerful tools can be developed for general systems analysis, which is particularly suitable for production systems. After describing the Simulation-based Innovization procedure and its difference from conventional simulation analysis methods, the results of an industrial case study, carried out for the improvement of an assembly line at an automotive manufacturer in Sweden, are presented.

  • 118.
    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.

  • 119.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    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.
    A Platform for Metamodel-Assisted Parallel Simulation Optimisation using Soft Computing Techniques2007In: The 24th annual workshop of the Swedish Artificial Intelligence Society (SAIS2007), Borås: University College of Borås , 2007, p. 181-184Conference paper (Refereed)
  • 120.
    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.

  • 121.
    Ng, Amos H. C.
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden.
    What Does Multi-Objective Optimization Have to Do with Bottleneck Improvement of Production Systems?2014In: Proceedings of The 6th International Swedish Production Symposium 2014 / [ed] Johan Stahre, Björn Johansson & Mats Björkman, 2014Conference paper (Refereed)
    Abstract [en]

    Bottleneck is a common term used to describe the process/operation/person that constrains the performance of the whole system. Since Goldratt introduced his theory of constraint, not many will argue about the importance of identifying and then improving the bottleneck, in order to improve the performance of the entire system. Nevertheless, there exist various definitions of bottleneck, which make bottleneck identification and improvement not a straightforward task in practice. The theory introduced by Production Systems Engineering (PSE) that the bottleneck of a production line is where the infinitesimal improvement can lead to the largest improvement of the average throughput, has provided an inspirational and rigorous way to understand the nature of bottleneck. This is because it conceptually puts bottleneck identification and improvement into a single task. Nevertheless, it is said that a procedure to evaluate how the efficiency increase of each machine would affect the total performance of a line is hardly possible in most practical situations. But is this true?In this paper, we argue how multi-objective optimization fits nicely into the theory introduced by PSE and hence how it can be developed into a practical bottleneck improvement methodology. Numerical results from a real-world application study on a highly complex machining line are provided to justify the practical applicability of this new methodology.

  • 122.
    Ng, Amos H. C.
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Bernedixen, Jacob
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Syberfeldt, Anna
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    A comparative study of production control mechanisms using simulation-based multi-objective optimisation2012In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 50, no 2, p. 359-377Article in journal (Refereed)
    Abstract [en]

    There exist many studies conducted to compare the performance of different production control mechanisms (PCMs) in order to determine which one performs the best under different conditions. Nonetheless, most of these studies suffer from the problems that the PCMs are not compared with their optimal parameter settings in a truly multi-objective context. This paper describes how different PCMs can be compared under their optimal settings through generating the Pareto-optimal frontiers, in the form of optimal trade-off curves in the performance space, by applying evolutionary multi-objective optimisation to simulation models. This concept is illustrated with a bi-objective comparative study of the four most popular PCMs in the literature, namely Push, Kanban, CONWIP and DBR, on an unbalanced serial flow line in which both control parameters and buffer capacities are to be optimised. Additionally, it introduces the use of normalised hyper-volume as the quantitative metric and confidence-based significant dominance as the statistical analysis method to verify the differences of the PCMs in the performance space. While the results from this unbalanced flow line cannot be generalised, it indicates clearly that a PCM may be preferable in certain regions of the performance space, but not others, which supports the argument that PCM comparative studies have to be performed within a Pareto-based multi-objective context.

  • 123.
    Ng, Amos H. C.
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Dudas, Catarina
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Boström, Henrik
    Stockholm University, Sweden.
    Kalyanmoy, Deb
    Michigan State University, USA.
    Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster ConvergenceOptimization2013In: Learning and Intelligent Optimization: 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers / [ed] Giuseppe Nicosia, Panos Pardalos, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2013, p. 1-18Chapter in book (Refereed)
    Abstract [en]

    This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, faster convergence to the desired solution of the decision-maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.

  • 124.
    Ng, Amos H. C.
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Dudas, Catarina
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Nießen, Johannes
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Deb, Kalyanmoy
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Simulation-Based Innovization Using Data Mining for Production Systems Analysis2011In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing / [ed] Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb, Springer London, 2011, p. 401-429Chapter in book (Refereed)
    Abstract [en]

    This chapter introduces a novel methodology for the analysis and optimization of production systems. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. Although the innovization method is based on multi-objective optimization and post-optimality analyses of optimised solutions, it stretches the scope beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. By integrating the concept of innovization with discrete-event simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis, particularly suitable for production systems. The uniqueness of the integrated approach proposed in this chapter lies on applying data mining to the data sets generated from simulation-based multi-objective optimization, in order to automatically or semi-automatically discover and interpret the hidden relationships and patterns for optimal production systems design/reconfiguration.

  • 125.
    Ng, Amos H. C.
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Dudas, Catarina
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Pehrsson, Leif
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Deb, Kalyanmoy
    University of Skövde, The Virtual Systems Research Centre.
    Knowledge Discovery in Production simulation By Interleaving Multi-Objective Optimization and Data Mining2012In: Proceedings of the SPS12 conference 2012, The Swedish Production Academy , 2012, p. 461-471Conference paper (Refereed)
  • 126.
    Ng, Amos H. C.
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Shaaban, Sabry
    Department of Strategy, ESC La Rochelle, La Rochelle, France.
    Bernedixen, Jacob
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation2017In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 55, no 24, p. 7435-7451Article in journal (Refereed)
    Abstract [en]

    Numerous studies have investigated the effects of unbalanced service times and inter-station buffer sizes on the efficiency of discrete part, unpaced production lines. There are two main disadvantages of many of these studies: (1) only some predetermined degree of imbalance and patterns of imbalance have been evaluated against the perfectly balanced configuration, making it hard to form a general conclusion on these factors; (2) only a single objective has been set as the target, which neglects the fact that different patterns of imbalance may outperform with respect to different performance measures. Therefore, the aim of this study is to introduce a new approach to investigate the performance of unpaced production lines by using multiple-objective optimisation. It has been found by equipping multi-objective optimisation with an efficient, equality constraints handling technique, both the optimal pattern and degree of imbalance, as well as the optimal relationship among these factors and the performance measures of a production system can be sought and analysed with some single optimisation runs. The results have illustrated that some very interesting relationships among the key performance measures studied, including system throughput, work-in-process and average buffer level, could only be observed within a truly multi-objective optimisation context. While these results may not be generalised to apply to any production lines, the genericity of the proposed simulation-based approach is believed to be applicable to study any real-world, complex production lines.

  • 127. Ng, Amos H. C.
    et al.
    Urenda Moris, Matias
    Svensson, Jacob
    Multi-Objective Simulation Optimization for Production Systems Design using FACTS Analyser2008In: Proceedings of the 2nd Swedish Production Symposium, 2008, p. 101-109Conference paper (Refereed)
    Abstract [en]

    This paper proposes a new general method for supporting production systems design within the context of Multi-objective Simulation Optimisation. Under this framework, different Production Control Mechanisms can be compared based on their optimal settings, which will be illustrated with a pedagogical simple flow line as well as an engines assembly line in automotive industry. Results from these case studies have provided significant insight into the importance of applying MOSO for Multi-Criteria Decision Making in general production systems design. At the same time, it also outlines the concept of applying significant dominance to handle uncertainty from stochastic simulation output, which has been implemented into a Web-based DES system called FACTS Analyser, specifically designed for conceptual factory design, analysis and optimisation.

  • 128.
    Ng, Amos H.C.
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. School of Engineering, Jönköping University, Sweden .
    Bandaru, Sunith
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Frantzén, Marcus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden .
    Innovative Design and Analysis of Production Systems by Multi-objective Optimization and Data Mining2016In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 50, p. 665-671Article in journal (Refereed)
    Abstract [en]

    This paper presents an innovative approach for the design and analysis of production systems using multi-objective optimization and data mining. The innovation lies on how these two methods using different computational intelligence algorithms can be synergistically integrated and used interactively by production systems designers to support their design decisions. Unlike ordinary optimization approaches for production systems design which several design objectives are linearly combined into a single mathematical function, multi-objective optimization that can generate multiple design alternatives and sort their performances into an efficient frontier can enable the designer to have a more complete picture about how the design decision variables, like number of machines and buffers, can affect the overall performances of the system. Such kind of knowledge that can be gained by plotting the efficient frontier cannot be sought by single-objective based optimizations. Additionally, because of the multiple optimal design alternatives generated, they constitute a dataset that can be fed into some data mining algorithms for extracting the knowledge about the relationships among the design variables and the objectives. This paper addresses the specific challenges posed by the design of discrete production systems for this integrated optimization and data mining approach and then outline a new interactive data mining algorithm developed to meet these challenges, illustrated with a real-world production line design example.

  • 129.
    Ng, Amos H.C.
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Skoogh, Anders
    Chalmers University of Technology, Gothenburg, Sweden.
    Lämkull, Dan
    Volvo Car Corporation, Gothenburg, Sweden.
    Optimal Maintenance Resources Allocation Using Automated Simulation-based Optimisation and Data Management2015In: Simulation in Production and Logistics 2015 / [ed] Markus Rabe, Uwe Clausen, Stuttgart: Fraunhofer IRB Verlag, 2015, p. 437-446Conference paper (Refereed)
    Abstract [en]

    This paper introduces a Streamlined Modelling and Decision Support (StreaMod) approach in which input data management, simulation model generation/update and simulation-based optimisation are synergistically integrated into a largely automated process. The aim of this automated process is to support decision making related to the optimal maintenance resources allocation that could improve the performance of production/logistics systems. The basic novelty of the StreaMod optimisation methodology lies on the formulation of an optimal maintenance allocation problem of a production/logistic system into a bi-objective optimisation problem, so that optimal resources/changes to improve the throughput of the system can be sought in a single optimisation run. The successful application of this methodology in a real-world automotive factory will also be addressed in this paper.

  • 130.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Jägstam, Mats
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    Volvo Car Corporation.
    Deb, Kalyanmoy
    Indian Institute of Technology Kanpur, India.
    Improving Factory Productivity and Energy Efficiency Through Holistic Simulation Optimisation2011In: The 21st International Conference on Multiple Criteria Decision Making, University of Jyväskylä , 2011, p. 235-Conference paper (Refereed)
    Abstract [en]

    There is an urgent need for the automotive inductry to explore strategies and methods to accelerate the industrial efficiency progress and support decision making in order to regain profitability. At the same time, decision making should not be made strictly from a view of productivity and investment cost. Manufactures worldwide are taking steps towards more sustainable manufacturing. Sustainability, in terms of "Energy Efficiency", "Lean", "Lead Time Efficiency" and other forms of reuse/conservation of resources has become a paramount factor that needs to be considered not only during the operational stage but from the very first day a production system is designed or configured. Therefore, to optimise a manufacturing system today is not only about maximising capacity and minimising costs, it is also about minimising energy use, minimising loss, minimising manufacturing lead time and other sustainability measures. The aim of the presentation is to introduce an innovative simulation-based optimisation and knowledge elicitation methodology for decision-making support within the production systems lifecycle to increase the profitability (increasing cost effectiveness) and simultaneously sustainability (increasing energy efficiency, reducing losses/wastes and shorten Order to Delivery Time) of the Swedish manufacturing industry. The methodology is so called Holistic Simulation Optimisation (HSO) because unlike today's industrial practice that productivity, cost and sustainability are optimised separately, the framework proposed takes into account productivity, cost, and sustainability in a multi-level and multi-objective context. The HSO methodology is realised through the development of a software toolset that synergistically integrates Discrete Event Simulation with the sustainability and cost models that have been developed or extended by industrial companies with state-of-the-art multi-objective optimisation and data mining technologies. The potential benefits of using the HSO methodology on the efficiency of the production systems that are measurable and can be verified quantitatively are: 5-15% increase in productivity; 10-20% reduction in manufacturing lead time; reduction in environmental wastes, in terms of energy use and other forms of losses (10-20%). The paper will present how these estimations are based on the case studies conducted in Swedish automotive industry.

  • 131.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Persson, Jacob
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matias
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Introducing Simulation-based Optimization for Production Systems Design to Industry: the FACTS Game2008In: Proceedings of the 18th International Conference on Flexible Automation and Intelligent Manufacturing: FAIM 2008, June 30th – July 2nd, 2008 University of Skövde, Sweden, Skövde: University of Skövde , 2008, p. 1359-1372Conference paper (Refereed)
  • 132.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Grimm, Henrik
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Svensson, Jacob
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Multi-Objective Simulation Optimization and Significant Dominance for Comparing Production Control Mechanisms2008In: Proceedings of the 18th International Conference on Flexible Automation and Intelligent Manufacturing, Skövde, Sweden, 2008, Skövde: University of Skövde , 2008Conference paper (Refereed)
  • 133.
    Ng, Amos
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Urenda Moris, Matias
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Svensson, Jacob
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    An Internet-Enabled Tool for Multi-Objective Simulation Optimization2009In: OPTIMA 2009: VIII Congreso Chileno de Investigacion Operativa, 2009Conference paper (Refereed)
  • 134.
    Ng, Amos
    et al.
    University of Skövde, School of Technology and Society.
    Urenda Moris, Matías
    University of Skövde, School of Technology and Society.
    Svensson, Jacob
    University of Skövde, School of Technology and Society.
    Skoogh, Anders
    Chalmers tekniska högskola, Institutionen för produkt- och produktionsutveckling, Produktionssystem.
    Johansson, Björn
    Chalmers tekniska högskola, Institutionen för produkt- och produktionsutveckling, Produktionssystem.
    FACTS Analyser: An innovative tool for factory conceptual design using simulation2007Conference paper (Refereed)
    Abstract [en]

    Despite simulation possesses an established background and offers tremendous promise for designing and analysing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conceptual phase of factory design. If simulation is used today for system design, it is more often used in later phases when important design decisions have already been made and costs are locked. With an aim to advocate the use of simulation in early phases of factory design, this paper introduces FACTS Analyser, a toolset developed based on the concept of integrating model abstraction, input data management and simulation-based optimisation under an innovative framework. Specifically, it addresses a novel aggregation method, which is based on Effective Processing Time, for modelling variability of workstations. Other features like simulation model generation, parallel simulation, optimisation and output data analysis that are provided by FACTS Analyser, through a Web Services interface, are also revealed. The aggregation method, when combined together with other system components, can synthetically enable simulation to become easier to use and speed up the time-consuming model building and experimentation process, which are required in conceptual design phases of production systems. Initial validation results applied to a trucks assembly plant is also given.

  • 135.
    Nourmohammadi, Amir
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Choosing efficient meta-heuristics to solve the assembly line balancing problem: A landscape analysis approach2019In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 81, p. 1248-1253Article in journal (Refereed)
    Abstract [en]

    It is widely known that the assembly line balancing problem (ALBP) is an NP-hard optimization problem. Although different meta-heuristics have been proposed for solving this problem so far, there is no convincing support that what type of algorithms can perform more efficiently than the others. Thus, using some statistical measures, the landscape of the simple ALBP is studied for the first time in the literature. The results indicate a flat landscape for the problem where the local optima are uniformly scattered over the search space. Accordingly, the efficiency of population-based algorithms in addressing the considered problem is statistically validated.

  • 136.
    Nourmohammadi, Amir
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Fathi, Masood
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ruiz Zúñiga, Enrique
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    A Genetic Algorithm for Bi-Objective Assembly Line Balancing Problem2019In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 519-524Conference paper (Refereed)
    Abstract [en]

    Assembly line designs in manufacturing commonly face the key problem of dividing the assembly tasks among the working stations so that the efficiency of the line is optimized. This problem is known as the assembly line balancing problem which is known to be NP-hard. This study, proposes a bi-objective genetic algorithm to cope with the assembly line balancing problem where the considered objectives are the utilization of the assembly line and the workload smoothness measured as the line efficiency and the variation of workload, respectively. The performance of the proposed genetic algorithm is tested through solving a set of standard problems existing in the literature. The computational results show that the genetic algorithm is promising in providing good solutions to the assembly line balancing problem.

  • 137. Oldefors, Fredrik
    et al.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    PLC Verification and Monitoring Using 3D Graphical Simulation2004In: Proceedings of the 21st international manufacturing conference: IMC 21: servicing manufacturing: 1st to 3rd September 2004 / [ed] Pat Phelan, Limerick: University of Limerick. Dept. of Manufacturing and Operations Engineering , 2004, p. 219-224Conference paper (Other academic)
  • 138.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Research and Technology Development, Engine Manufacturing Engineering, Volvo Car Group.
    Frantzén, Marcus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aggregated line modeling for simulation and optimization of manufacturing systems2015In: 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 (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.

  • 139.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Gothenburg, Sweden.
    Karlsson, Ingemar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Towards Automated Multi-Objective Rule Extraction2016In: 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 (Refereed)
  • 140.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Gothenburg, Sweden.
    Lidberg, Simon
    Volvo Car Corporation, Gothenburg, Sweden.
    Frantzén, Marcus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aggregated Discrete Event Modelling for Simulation and Optimisation of Manufacturing Systems2014In: Industrial Simulation Conference, Skövde, June 11-13, 2014, Eurosis, 2014, 2014, p. 83-90Conference paper (Refereed)
    Abstract [en]

    In many simulation studies for factory analysis, for example, to locate the constraint of an entire factory that consists of multiple production lines, it may not be effective to put every process detail into a single model. Firstly, to develop such a factory-wide model would be very time-consuming. Secondly, it can be very computational costly to run the model, especially if simulation-based optimisation is applied to find the optimal setting from such a complex model that possesses all the details of the processes. In this regard, aggregation, with which multiple process steps are aggregated into some simpler simulation objects, is an effective method to reduce both the development and computational times. On one hand, based on the initial analysis, the simulation expert can pinpoint the sub-system that restrains the performance of the entire factory and decide if a more detailed model is needed. On the other hand, interline buffers/storages can be readily optimised by using such an aggregated model. Through an application study with data from a real-world factory, this paper introduces a novel aggregation method and illustrates the potential of the abovesaid concepts.

  • 141.
    Pehrsson, Leif
    et al.
    Volvo Car Corporation, Gothenburg, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Decision-making in conceptual AGV systems design using simulation-based optimization2013In: Proceedings of Industrial Simulation Conference, May 22-24, Ghent, Belgium, 2013, p. 171-176Conference paper (Refereed)
  • 142.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis2016In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, p. 24-37Article in journal (Refereed)
    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.

  • 143.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Multi-objective production system optimisation including investment and running costs2011In: Proceedings of the 4th Swedish Production Symposium, SPS11, May 3-5, Lund, Sweden, Lund, 2011Conference paper (Refereed)
  • 144.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Multi-objective Production Systems Optimisation with Investment and Running Cost2011In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing / [ed] Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb, Springer London, 2011, p. 431-453Chapter in book (Refereed)
    Abstract [en]

    In recent years simulation-based multi-objective optimisation (SMO) of production systems targeting e.g., throughput, buffers and work-in-process (WIP) has been proven to be a very promising concept. In combination with post-optimality analysis, the concept has the potential of creating a foundation for decision support. This chapter will explore the possibility to expand the concept of introducing optimisation of production system cost aspects such as investments and running cost. A method with a procedure for industrial implementation is presented, including functions for running cost estimation and investment combination optimisation. The potential of applying SMO and postoptimality analysis, taking into account both productivity and financial factors for decision-making support, has been explored and proven to be very beneficial for this kind of industrial application. Evaluating several combined minor improvements with the help of SMO has opened the opportunity to identify a set of solutions (designs) with great financial improvement, which are not feasible to be explored by using current industrial procedures.

     

  • 145.
    Pehrsson, Leif
    et al.
    Volvo Car Corporation, Gothenburg, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Stockton, David
    De Montfort University, Leicester, United Kingdom.
    Sectioned Walking Worker Lines with Loop Balancing2013Conference paper (Refereed)
  • 146.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Stockton, David
    Centre for Manufacturing, De Montfort University, Leicester, UK.
    Industrial cost modelling and multi-objective optimisation for decision support in production systems development2013In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 66, no 4, p. 1036-1048Article in journal (Refereed)
    Abstract [en]

    Recent developments in cost modelling, simulation-based multi-objective optimisation, and post-optimality analysis have enabled the integration of costing data and cost estimation into a new methodology for supporting economically sound decision-making in manufacturing enterprises. Within this methodology, the combination of production engineering and financial data with multi-objective optimisation and post-optimality analysis has been proven to provide the essential information to facilitate knowledge-driven decision-making in real-world production systems development. The focus of this paper is to present the incremental cost modelling technique specifically designed for the integration with discrete-event simulation models and multi-objective optimisation within this methodology. A complete example, using the simulation model and data modified from a previous real-world case study, is provided in this paper to illustrate how the methodology and cost modelling are applied for the optimal investment decision support. (C) 2013 Elsevier Ltd. All rights reserved.

  • 147.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Gothenburg, Sweden.
    Ng, Amos. H.C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    An Applied Framework for simulation-based multi-objective optimisation within production system development2011In: Proceedings of the 9th Industrial Simulation Conference, Eurosis , 2011, p. 121-128Conference paper (Refereed)
    Abstract [en]

    A method and a framework for the application of Simulation-based Multi-objective Optimisation (SMO) has been developed in order to enhance the prerequisites for decision-making within design and re-configuration of production systems. This kind of decisions often tends to be based on financial information rather than the type of production system parameters to be found in traditional simulation models. Therefore, to combine traditional parameters with new financial and sustainability parameters can be very beneficial for supporting industrial decision-making. The framework has been applied in a number of case studies involving a range of production system issues both within component production and assembly operations. Several types of issues have been explored involving analysis of system behaviour, optimisation of sustainability parameters, in the form of energy consumption aggregated to energy cost, and optimisation of financial parameters in combination with traditional production system metrics. The case studies have adequately proven and verified that the application of SMO, especially including financial functions and objectives, can be very valuable for practical industrial applications.

  • 148.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Andersson, Marcus
    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.
    Metamodel-Assisted Simulation-Based Optimization of a Real-World Manufacturing Problem2007In: The 17th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2007, 2007, p. 950-956Conference paper (Refereed)
  • 149.
    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.
    A Metamodel-Assisted Steady-State Evolution Strategy for Simulation-Based Optimization2007In: Trends in Intelligent Systems and Computer Engineering / [ed] Oscar Castillo, Li Xu, Sio-Iong Ao, Springer Science+Business Media B.V., 2007, p. 1-13Chapter in book (Refereed)
  • 150.
    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.
    Metamodel-assisted Global Search Using a Probing Technique2007In: The IAENG International Conference on Artificial Intelligence and Applications (ICAIA'07), International Association of Engineers, 2007, p. 83-88Conference paper (Refereed)
    Abstract [en]

    This paper presents a new metamodel-assisted metaheuristic algorithm for optimisation problems involving computationally expensive simulations. The algorithm, called Global Probing Search, is a population-based algorithm designed for global optimisation. The main idea of the algorithm is to introduce a probing phase in the creating of the new generation of the population. In this probing phase, a large number of candidate solutions are generated and a computationally cheap metamodel function is used for choosing the most promising candidates to transfer to the next generation. This approach could significantly enhance the efficiency of the optimisation process by avoiding wasting valuable evaluation time on solutions that are likely to be inferior. During the optimisation, the accuracy of the metamodel is constantly improved through on-line updating. The proposed algorithm is implemented on a real-world optimisation problem and initial results indicate that the algorithm show good performance in comparison with a standard Genetic Algorithm and an existing metamodel-assisted metaheuristic.

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