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  • 1.
    Andersson, Martin
    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.
    On the Trade-off Between Runtime and Evaluation Efficiency In Evolutionary AlgorithmsIn: Evolutionary Computation, ISSN 1063-6560, E-ISSN 1530-9304Article in journal (Refereed)
    Abstract [en]

    Evolutionary optimization algorithms typically use one or more parameters that control their behavior. These parameters, which are often kept constant, can be tuned to improve the performance of the algorithm on specific problems.  However, past studies have indicated that the performance can be further improved by adapting the parameters during runtime. A limitation of these studies is that they only control, at most, a few parameters, thereby missing potentially beneficial interactions between them. Instead of finding a direct control mechanism, the novel approach in this paper is to use different parameter sets in different stages of an optimization. These multiple parameter sets, which remain static within each stage, are tuned through extensive bi-level optimization experiments that approximate the optimal adaptation of the parameters. The algorithmic performance obtained with tuned multiple parameter sets is compared against that obtained with a single parameter set.  For the experiments in this paper, the parameters of NSGAII are tuned when applied to the ZDT, DTLZ and WFG test problems. The results show that using multiple parameter sets can significantly increase the performance over a single parameter set.

  • 2.
    Bernedixen, Jacob
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Automated Bottleneck Analysis of Production Systems: Increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Manufacturing companies constantly need to explore new management strategies and new methods to increase the efficiency of their production systems and retain their competitiveness. It is of paramount importance to develop new bottleneck analysis methods that can identify the factors that impede the overall performance of their productionsystems so that the optimal improvement actions can be performed. Many of the bottleneck-related research methods developed in the last two decades are aimed mainly at detecting bottlenecks. Due to their sole reliance on historical data and lackof any predictive capability, they are less useful for evaluating the effect of bottleneck improvements.

    There is an urgent need for an efficient and accurate method of pinpointing bottlenecks, identifying the correct improvement actions and the order in which these should be carried out, and evaluating their effects on the overall system performance. SCORE (simulation-based constraint removal) is a novel method that uses simulation based multi-objective optimization to analyze bottlenecks. By innovatively formulating bottleneck analysis as a multi-objective optimization problem and using simulation to evaluate the effects of various combinations of improvements, all attainable, maximum throughput levels of the production system can be sought through a single optimization run. Additionally, post-optimality frequency analysis of the Pareto-optimal solutions can generate a rank order of the attributes of the resources required to achieve the target throughput levels. However, in its original compilation, SCORE has a very high computational cost, especially when the simulation model is complex with a large number of decision variables. Some tedious manual setup of the simulation based optimization is also needed, which restricts its applicability within industry, despite its huge potential. Furthermore, the accuracy of SCORE in terms of convergence in optimization theory and correctness of identifying the optimal improvement actions has not been evaluated scientifically.

    Building on previous SCORE research, the aim of this work is to develop an effective method of automated, accurate bottleneck identification and improvement analysis that can be applied in industry.

    The contributions of this thesis work include:

    (1) implementation of a versatile representation in terms of multiple-choice set variables and a corresponding constraint repair strategy into evolutionary multi-objective optimization algorithms;

    (2) introduction of a novel technique that combines variable screening enabled initializationof population and variable-wise genetic operators to support a more efficient search process;

    (3) development of an automated setup for SCORE to avoid the tedious manual creation of optimization variables and objectives;

    (4) the use of ranking distance metrics to quantify and visualize the convergence and accuracy of the bottleneck ranking generated by SCORE.

    All these contributions have been demonstrated and evaluated through extensive experiments on scalable benchmark simulation models as well as several large-scale simulation models for real-world improvement projects in the automotive industry.

    The promising results have proved that, when augmented with the techniques proposed in this thesis, the SCORE method can offer real benefits to manufacturing companies by optimizing their production systems.

  • 3.
    Bernedixen, Jacob
    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.
    Multiple Choice Sets and Manhattan Distance Based Equality Constraint Handling for Production Systems OptimizationIn: Computers & Operations Research, ISSN 0305-0548, E-ISSN 1873-765XArticle in journal (Refereed)
    Abstract [en]

    Many simulation-based optimization packages provide powerful algorithms to solve industrialproblems. But most of them fail to oer their users the techniques they needto eectively handle multiple-choice problems involving a large set of decision variableswith mixed types (continuous, discrete and combinatorial) and problems that are highlyconstrained (e.g., with many equality constraints). Yet such issues are found in manyreal-world production system design and improvement problems. Thus, this paper introducesa method to eectively embed multiple choice sets and Manhattan-distancebasedconstraint handling into multi-objective optimization algorithms like NSGA-II andNSGA-III. This paper illustrates and evaluates how these two techniques have been appliedtogether to solve optimal workload, buer and workforce allocation problems. Anexample follows, showing their application to a complex production system improvementproblem at an automotive manufacturer.

  • 4.
    Bernedixen, Jacob
    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.
    Practical Production Systems Optimization Using Multiple-Choice Sets and Manhattan Distance based Constraints Handling2014In: Industrial Simulation Conference, Skövde, June 11-13, 2014, 2014, p. 97-103Conference paper (Refereed)
    Abstract [en]

    Many simulation-based optimization packages provide powerful algorithms to solve large-scale system problems. But most of them fall short to offer their users the techniques to effectively handle decision variables that are of multiple-choice type, as well as equality constraints, which can be found in many real-world industrial system design and improvement problems. Hence, this paper introduces how multiple choice sets and Manhattan-distance-based constraint handling can be effectively embedded into a meta-heuristic algorithm for simulation-based optimization. How these two techniques have been applied together to make the improvement of a complex production system, provided by an automotive manufacturer, possible will also be presented.

  • 5.
    Bernedixen, Jacob
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bandaru, Sunith
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    On the convergence of stochastic simulation-based multi-objective optimization for bottleneck identificationIn: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588XArticle in journal (Refereed)
    Abstract [en]

    By innovatively formulating a bottleneck identication problem into a bi-objective optimization,simulation-based multi-objective optimization (SMO) can be eectively used as a new method for gen-eral production systems improvement. In a single optimization run, all attainable, maximum throughputlevels of the system can be sought through various optimal combinations of improvement changes ofthe resources. Additionally, the post-optimality frequency analysis on the Pareto-optimal solutions cangenerate a rank order of the attributes of the resources required to achieve the target throughput levels.Observing that existing research mainly put emphasis on measuring the convergence of the optimizationin the objective space, leaving no information on when the solutions in the decision space have convergedand stabilized, this paper represents the rst eort in increasing the knowledge about the convergence ofSMO for the rank ordering in the context of bottleneck analysis. By customizing the Spearman's footruledistance and Kendall's tau, this paper presents how these metrics can be used eectively to provide thedesired visual aid in determining the convergence of bottleneck ranking, hence can assist the user todetermine correctly the terminating condition of the optimization process. It illustrates and evaluatesthe convergence of the SMO for bottleneck analysis on a set of scalable benchmark models as well as twoindustrial simulation models. The results have shed promising direction of applying these new metrics tocomplex, real-world applications.

  • 6.
    Bernedixen, Jacob
    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.
    Bandaru, Sunith
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Variables Screening Enabled Multi-Objective Optimization for Bottleneck Analysis of Production SystemsManuscript (preprint) (Other academic)
    Abstract [en]

    Bottleneck analysis can be defined as the process that includes both bottleneck identification and improvement. In the literature most of the proposed bottleneck-related methods address mainly bottleneck detection. By innovatively formulating a bottleneck analysis into a bi-objective optimization method, recent research has shown that all attainable, maximized TH of a production system, through various combinations of improvement changes of the resources, can be sought in a single optimization run. Nevertheless, when applied to simulation-based evaluation, such a bi-objective optimization is computationally expensive especially when the simulation model is complex and/or with a large amount of decision variables representing the improvement actions. The aim of this paper is therefore to introduce a novel variables screening enabled bi-objective optimization that is customized for bottleneck analysis of production systems. By using the Sequential Bifurcation screening technique which is particularly suitable for large-scale simulation models, fewer simulation runs are required to find the most influenacing factors in a simulation model. With the knowledge of these input variables, the bi-objective optimization used in the bottleneck analysis can customize the genetic operators on these variables individually according to their rank of main effects with the target to speed up the entire optimization process. The screening-enabled algorithm is then applied to a set of experiments designed to evaluate how well it performs when the number of variables increases is a scalable, benchmark model, as well as two real-world industrial-scale simulation models found in the automotive industry. The results have illustrated the promising direction of incorporating the knowledge of influencing variables and variable-wise genetic operators into a multi-objective optimization algorithm for bottleneck analysis.

  • 7.
    Bernedixen, Jacob
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Gothenburg, Sweden.
    Antonsson, Tobias
    Volvo Car Corporation, Gothenburg, Sweden.
    Simulation-based multi-objective bottleneck improvement: Towards an automated toolset for industry2015In: 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, Press Piscataway, NJ: IEEE Press, 2015, p. 2183-2194Conference paper (Refereed)
    Abstract [en]

    Manufacturing companies of today are under pressure to run their production most efficiently in order to sustain their competitiveness. Manufacturing systems usually have bottlenecks that impede their performance, and finding the causes of these constraints, or even identifying their locations, is not a straightforward task. SCORE (Simulation-based COnstraint REmoval) is a promising method for detecting and ranking bottlenecks of production systems, that utilizes simulation-based multi-objective optimization (SMO). However, formulating a real-world, large-scale industrial bottleneck analysis problem into a SMO problem using the SCORE-method manually include tedious and error-prone tasks that may prohibit manufacturing companies to benefit from it. This paper presents how the greater part of the manual tasks can be automated by introducing a new, generic way of defining improvements of production systems and illustrates how the simplified application of SCORE can assist manufacturing companies in identifying their production constraints.

  • 8.
    Bernedixen, Jacob
    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.
    Optimal Buffer Allocation for Semi-synchronized Automotive Assembly Lines using Simulation-based Multi-objective Optimization2011In: Proceedings of the 9th Industrial Simulation Conference, Eurosis , 2011, p. 129-135Conference paper (Refereed)
    Abstract [en]

    A practical question in industry in designing or re-designing a production system is: how small can intermediated buffers be to ensure the desired production rate? This topic is usually called optimal buffer allocation as the goal is to allocate the minimum buffer capacities to optimize the performance of the line. This paper presents a case study of using simulation-based evolutionary multi-objective optimization to determine the optimal buffer capacities and positions in the reconfiguration of a real-world truck axle assembly line in an automobile manufacturer. The case study has not only revealed the applicability of the methodology in seeking optimal configurations in a truly multi-objective context, it also illustrates how additional important knowledge was gained by analyzing the optimization results in the objective space.

  • 9.
    Karlsson, Ingemar
    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.
    Ng, Amos H. C.
    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.
    Combining augmented reality and simulation-based optimization for decision support in manufacturing2017In: Proceedings of the 2017 Winter Simulation Conference / [ed] W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3988-3999Conference paper (Refereed)
    Abstract [en]

    Although the idea of using Augmented Reality and simulation within manufacturing is not a new one, the improvement of hardware enhances the emergence of new areas. For manufacturing organizations, simulation is an important tool used to analyze and understand their manufacturing systems; however, simulation models can be complex. Nonetheless, using Augmented Reality to display the simulation results and analysis can increase the understanding of the model and the modeled system. This paper introduces a decision support system, IDSS-AR, which uses simulation and Augmented Reality to show a simulation model in 3D. The decision support system uses Microsoft HoloLens, which is a head-worn hardware for Augmented Reality. A prototype of IDSS-AR has been evaluated with a simulation model depicting a real manufacturing system on which a bottleneck detection method has been applied. The bottleneck information is shown on the simulation model, increasing the possibility of realizing interactions between the bottlenecks. 

  • 10.
    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.

  • 11.
    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.

  • 12.
    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.

  • 13.
    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.

  • 14. 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.

  • 15.
    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)
  • 16.
    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)
  • 17.
    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)
  • 18.
    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.

  • 19.
    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.

  • 20.
    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)
  • 21.
    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.

     

  • 22.
    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)
  • 23.
    Siegmund, Florian
    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.
    Pehrsson, Leif
    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.
    Deb, Kalyanmoy
    Department of Mechanical Engineering, Indian Institute of Technology Kanpur, India.
    Reference point-based evolutionary multi-objective optimization for industrial systems simulation2012In: Proceedings of the 2012 Winter Simulation Conference (WSC) / [ed] C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, IEEE conference proceedings, 2012Conference paper (Refereed)
    Abstract [en]

    In Multi-objective Optimization the goal is to present a set of Pareto-optimal solutions to the decision maker (DM). One out of these solutions is then chosen according to the DM preferences. Given that the DM has some general idea of what type of solution is preferred, a more efficient optimization could be run. This can be accomplished by letting the optimization algorithm make use of this preference information and guide the search towards better solutions that correspond to the preferences. One example for such kind of algorithms is the reference point-based NSGA-II algorithm (R-NSGA-II), by which user-specified reference points can be used to guide the search in the objective space and the diversity of the focused Pareto-set can be controlled. In this paper, the applicability of the R-NSGA-II algorithm in solving industrial-scale simulation-based optimization problems is illustrated through a case study of the improvement of a production line.

  • 24.
    Skoogh, Anders
    et al.
    Chalmers University of Technology, Gothenburg, Sweden.
    André, Jean-Patrick
    Chalmers University of Technology, Gothenburg, Sweden.
    Dudas, Catarina
    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.
    Urenda Moris, Matias
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Johansson, Björn
    Chalmers University of Technology, Gothenburg, Sweden.
    An automated approach to input data management in discrete event simulation projects: A proof-of-concept demonstrator2007In: EUROSIM 2007, 2007Conference paper (Refereed)
    Abstract [en]

    Despite the fact that Discrete Event Simulation (DES) is claimed to be one of the most potent tools for analysis and optimization of production systems, industries worldwide have not been able to fully utilize its potential. One reason is argued to be that DES projects are not time efficient enough due to extensive time consumption during the input data phases. In some companies, input data is totally missing, but even in projects where data is available it usually takes a considerable amount of time to analyze and prepare it for use in a simulation model. This paper presents one approach to the problem by implementing a software that automates several steps in the input data process such as extracting data from a database, sorting out the information needed and fitting the data to statistical distributions. The approach and the software have been developed based on a case study at Volvo Trucks in Gothenburg, Sweden. The work presented in this paper is part of a more comprehensive project called FACTS. The project scope is to develop methods and IT-tools for conceptual plant development. 

  • 25.
    Urenda Moris, Matias
    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.
    Svensson, Jacob
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Simplification and aggregation strategies applied for factory analysis in conceptual phase using simulation2008In: Proceedings of the 2008 Winter Simulation Conference, IEEE conference proceedings, 2008, p. 1913-1921Conference paper (Refereed)
  • 26.
    Urenda Moris, Matías
    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.
    Goienetxea, Ainhoa
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Diseño Y Análisis De Sistemas Productivos Utilizando La Optimización Mediante Simulación Basado En Internet2012In: Ingenieria Industrial, ISSN 0717-9103, E-ISSN 0718-8307, Vol. 11, no 1, p. 37-49Article in journal (Other academic)
  • 27.
    Urenda Moris, Matías
    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.
    Goienetxea Uriarte, Ainhoa
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Diseño Y Análisis De Sistemas Productivos Utilizando La Optimización Mediante Simulación Basado En Internet2011Conference paper (Refereed)
1 - 27 of 27
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