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  • 1.
    Andersson, Marcus
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Persson, Anna
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    A web-based simulation optimization system for industrial scheduling2007In: Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come, IEEE Press, 2007, p. 1844-1852Conference paper (Refereed)
    Abstract [en]

    Many real-world production systems are complex in nature and it is a real challenge to find an efficient scheduling method that satisfies the production requirements as well as utilizes the resources efficiently. Tools like discrete event simulation (DES) are very useful for modeling these systems and can be used to test and compare different schedules before dispatching the best schedules to the targeted systems. DES alone, however, cannot be used to find the "optimal" schedule. Simulation-based optimization (SO) can be used to search for optimal schedules efficiently without too much user intervention. Observing that long computing time may prohibit the interest in using SO for industrial scheduling, various techniques to speed up the SO process have to be explored. This paper presents a case study that shows the use of a Web-based parallel and distributed SO platform to support the operations scheduling of a machining line in an automotive factory.

  • 2.
    Andersson, Marcus
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    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.
    Simulation Optimization for Industrial Scheduling Using Hybrid Genetic Representation2008In: Proceedings of the 2008 Winter Simulation Conference / [ed] S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler, IEEE conference proceedings, 2008, p. 2004-2011Conference paper (Refereed)
    Abstract [en]

    Simulation modeling has the capability to represent complex real-world systems in details and therefore it is suitable to develop simulation models for generating detailed operation plans to control the shop floor. In the literature, there are two major approaches for tackling the simulation-based scheduling problems, namely dispatching rules and using meta-heuristic search algorithms. The purpose of this paper is to illustrate that there are advantages when these two approaches are combined. More precisely, this paper introduces a novel hybrid genetic representation as a combination of both a partially completed schedule (direct) and the optimal dispatching rules (indirect), for setting the schedules for some critical stages (e.g. bottlenecks) and other non-critical stages respectively. When applied to an industrial case study, this hybrid method has been found to outperform the two common approaches, in terms of finding reasonably good solutions within a shorter time period for most of the complex scheduling scenarios.

  • 3.
    Andersson, Marcus
    et al.
    University of Skövde, School of Technology and Society.
    Persson, Anna
    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.
    Simulation-based Scheduling using a Genetic Algorithm with Consideration to Robustness: A Real-world Case Study2007In: Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2007), 2007, p. 957-964Conference paper (Refereed)
  • 4.
    Aslam, Tehseen
    et al.
    University of Skövde, School of Technology and Society.
    Andersson, Marcus
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    Simulation-Based Optimisation For Complex Production Systems2006In: IMC23, 2006, p. 519-526Conference paper (Other academic)
  • 5.
    Dudas, Catarina
    et al.
    University of Skövde, School of Technology and Society.
    Frantzén, Marcus
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Simulation-Based Innovization for the Analysis of a Machining Line2010In: 20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010): Volume 1 of 2, Curran Associates, Inc., 2010, p. 959-966Conference paper (Refereed)
  • 6.
    Dudas, Catarina
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Frantzén, Marcus
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos H.C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop2011In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 27, no 4, p. 687-695Article in journal (Refereed)
    Abstract [en]

    A method for analyzing production systems by applying multi-objective optimization and data mining techniques on discrete-event simulation models, the so-called Simulation-based Innovization (SBI) is presented in this paper. The aim of the SBI analysis is to reveal insight on the parameters that affect the performance measures as well as to gain deeper understanding of the problem, through post-optimality analysis of the solutions acquired from multi-objective optimization. This paper provides empirical results from an industrial case study, carried out on an automotive machining line, in order to explain the SBI procedure. The SBI method has been found to be particularly siutable in this case study as the three objectives under study, namely total tardiness, makespan and average work-in-process, are in conflict with each other. Depending on the system load of the line, different decision variables have been found to be influencing. How the SBI method is used to find important patterns in the explored solution set and how it can be valuable to support decision making in order to improve the scheduling under different system loadings in the machining line are addressed.

  • 7.
    Frantzén, Marcus
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    A real-time simulation-based optimisation environment for industrial scheduling2013Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In order to cope with the challenges in industry today, such as changes in product diversity and production volume, manufacturing companies are forced to react more flexibly and swiftly. Furthermore, in order for them to survive in an ever-changing market, they also need to be highly competitive by achieving near optimal efficiency in their operations. Production scheduling is vital to the success of manufacturing systems in industry today, because the near optimal allocation of resources is essential in remaining highly competitive.

     

    The overall aim of this study is the advancement of research in manufacturing scheduling through the exploration of more effective approaches to address complex, real-world manufacturing flow shop problems. The methodology used in the thesis is in essence a combination of systems engineering, algorithmic design and empirical experiments using real-world scenarios and data. Particularly, it proposes a new, web services-based, industrial scheduling system framework, called OPTIMISE Scheduling System (OSS), for solving real-world complex scheduling problems. OSS, as implemented on top of a generic web services-based simulation-based optimisation (SBO) platform called OPTIMISE, can support near optimal and real-time production scheduling in a distributed and parallel computing environment. Discrete-event simulation (DES) is used to represent and flexibly cope with complex scheduling problems without making unrealistic assumptions which are the major limitations of existing scheduling methods proposed in the literature.  At the same time, the research has gone beyond existing studies of simulation-based scheduling applications, because the OSS has been implemented in a real-world industrial environment at an automotive manufacturer, so that qualitative evaluations and quantitative comparisons of scheduling methods and algorithms can be made with the same framework.

     

    Furthermore, in order to be able to adapt to and handle many different types of real-world scheduling problems, a new hybrid meta-heuristic scheduling algorithm that combines priority dispatching rules and genetic encoding is proposed. This combination is demonstrated to be able to handle a wider range of problems or a current scheduling problem that may change over time, due to the flexibility requirements in the real-world.  The novel hybrid genetic representation has been demonstrated effective through the evaluation in the real-world scheduling problem using real-world data.

  • 8.
    Frantzén, Marcus
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    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.
    Karlsson, V.
    Volvo Group Trucks Operations, Powertrain Production Skövde, Skövde, Sweden.
    Bremert, M.
    Volvo Group Trucks Operations, Powertrain Production Skövde, Skövde, Sweden.
    Dynamic maintenance priority of a real-world machining line2016In: Proceedings of the 7th Swedish Production Symposium, 2016Conference paper (Refereed)
    Abstract [en]

    To support the shop-floor operators, decision support systems (DSS) are becoming more and more vital to the success of manufacturing systems in industry today. In order to get a DSS able to adapt to disturbances in a production system, on-line data are needed to be able to make optimal or near-optimal decisions in real-time (soft real-time). This paper investigates one part of such a system, i.e. how different priorities of maintenance activities (planned and unplanned) affect the productivity of a production system. A discrete-event simulation model has been built for a real-world machining line in order to simulate the decisions made in subject to disturbances. This paper presents a way of prioritizing operators and machines based on multiple criteria such as competence, utilization, distance, bottleneck, and Work-In-Process. An experimental study based on the real-world production system has shown promising results and given insights of how to prioritize the operators in a good way. Another novelty introduced in this paper is the use of simulation-based optimization to generate composite dispatching rules in order to find good tradeoffs when taking a decision of which machine or operator to select.

  • 9.
    Frantzén, Marcus
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    A case study of applying simulation-based optimisation to a real-world scheduling problem2010In: ORbit, ISSN 1601-8893, no 17, p. 4-7Article in journal (Other (popular science, discussion, etc.))
  • 10.
    Frantzén, Marcus
    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.
    Production simulation education using rapid modeling and optimization: Successful studies2015In: 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: IEEE Press, 2015, p. 3526-3537Conference paper (Refereed)
    Abstract [en]

    A common issue facing many simulation educators is that students usually spend excessive time to struggle with the programming and statistic parts of the simulation courses, and simply very little time to learn running systems analysis. If the students are coming from industry, and not the campus, then the problem becomes even worse. We observed this problem around 2005 and started to develop a new simulation software, a factory conceptual design toolset, partly aimed to address this problem. A new set of educational courses has since then been developed around the software for teaching production systems analysis, with both the campus students and managers/engineers from industry in mind. In this paper, we briefly introduce the software and share our experiences and some representative, successful studies conducted by the students in the past years.

  • 11.
    Frantzén, Marcus
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos H. C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Moore, Philip
    Computing Sciences and Engineering, De Montfort University Leicester, LE1 9BH, United Kingdom.
    A simulation-based scheduling system for real-time optimization and decision making support2011In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 27, no 4, p. 696-705Article in journal (Refereed)
    Abstract [en]

    This paper presents an industrial application of simulation-based optimization (SBO) in the scheduling and real-time rescheduling of a complex machining line in an automotive manufacturer in Sweden. Apart from generating schedules that are robust and adaptive, the scheduler must be able to carry out rescheduling in real time in order to cope with the system uncertainty effectively. A real-time scheduling system is therefore needed to support not only the work of the production planner but also the operators on the shop floor by re-generating feasible schedules when required. This paper describes such a real-time scheduling system, which is in essence a SBO system integrated with the shop floor database system. The scheduling system, called OPTIMISE scheduling system (OSS), uses real-time data from the production line and sends back expert suggestions directly to the operators through Personal Digital Assistants (PDAs). The user interface helps in generating new schedules and enables the users to easily monitor the production progress through visualization of production status and allows them to forecast and display target performance measures. Initial results from this industrial application have shown that such a novel scheduling system can help both in improving the line throughput efficiently and simultaneously supporting real-time decision making.

  • 12.
    Frantzén, Marcus
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Moore, Philip
    University of Skövde, School of Technology and Society.
    A Scheduling System for Real-Time Decision Making Support Using Simulation-Based Optimization2010In: 20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010): Volume 2 of 2, Curran Associates, Inc., 2010, p. 980-987Conference paper (Refereed)
  • 13.
    Holm, Magnus
    et al.
    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.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    Falmouth University, Penryn, Cornwall, United Kingdom.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    A methodology facilitating knowledge transfer to both research experienced companies and to novice SMEs2017In: International Journal of Enterprise Network Management, ISSN 1748-1252, Vol. 8, no 2, p. 123-140, article id IJENM0080202Article in journal (Refereed)
    Abstract [en]

    In this paper, knowledge transfer is defined as a process of disseminating both technological and theoretical understanding as well as enhancing both industrial and academic knowledge through conducted research to project partners collaborating within a research project. To achieve this, a new methodology called 'user groups' is introduced. It facilitates knowledge transfer between project participants in collaborative research programs engaging both experienced and unexperienced partners regardless of level of input. The introduced methodology 'user groups' provides tools for collaborating with several research partners even though their levels of engagement in the project and prior research experience may vary without dividing them into separate groups. It enables all project partners to gain new knowledge and by so doing extending the knowledge society. The case study shows that the eight engaged companies are able to cooperate, achieve their own objectives and, both jointly and individually, contribute to the overall project goals.

  • 14.
    Lidberg, Simon
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Skövde, Sweden.
    Pehrsson, Leif
    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.
    Applying Aggregated Line Modeling Techniques to Optimize Real World Manufacturing Systems2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 89-96Article in journal (Refereed)
    Abstract [en]

    The application of discrete event simulation methodology in the analysis of higher level manufacturing systems has been limited due to model complexity and the lack of aggregation techniques for manufacturing lines. Recent research has introduced new aggregation methods preparing for new approaches in the analysis of higher level manufacturing systems or networks. In this paper one of the new aggregated line modeling techniques is successfully applied on a real world manufacturing system, solving a real-world problem. The results demonstrate that the aggregation technique is adequate to be applied in plant wide models. Furthermore, in this particular case, there is a potential to reduce storage levels by over 25 %, through leveling the production flow, without compromising deliveries to customers.

  • 15.
    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.

  • 16.
    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.

  • 17.
    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)
  • 18.
    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.

  • 19.
    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.

  • 20.
    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.

  • 21.
    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.

  • 22.
    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)
  • 23.
    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.
    Andersson, Marcus
    University of Skövde, School of Technology and Society.
    Metamodel-Assisted Simulation-Based Optimisation of Manufacturing Systems2007In: Advances in manufacturing technology - XXI: proceedings of the 5th international conference on manufacturing research (ICMR2007) : 11th - 13th September 2007 / [ed] D. J. Stockton, R. A. Khalil & R. W. Baines, De Montfort university , 2007, p. 174-178Conference paper (Refereed)
1 - 23 of 23
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