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  • 1.
    Aslam, Tehseen
    et al.
    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
    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. Volvo Car Engine, Manufacturing Research and Concepts, Skövde, Sweden.
    Urenda-Moris, Mathias
    Uppsala University, Ångströmlaboratoriet, Uppsala, Sweden.
    Towards an industrial testbed for holistic virtual production development2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 369-374Conference paper (Refereed)
    Abstract [en]

    Virtual production development is adopted by many companies in the production industry and digital models and virtual tools are utilized for strategic, tactical and operational decisions in almost every stage of the value chain. This paper suggest a testbed concept that aims the production industry to adopt a virtual production development process with integrated tool chains that enables holistic optimizations, all the way from the overall supply chain performance down to individual equipment/devices. The testbed, which is fully virtual, provides a mean for development and testing of integrated digital models and virtual tools, including both technical and methodological aspects.

  • 2.
    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.

  • 3.
    Dudas, Catarina
    et al.
    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.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
    Integration of data mining and multi-objective optimisation for decision support in production system development2014In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 27, no 9, p. 824-839Article in journal (Refereed)
    Abstract [en]

    Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.

  • 4.
    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. 

  • 5.
    Lidberg, Simon
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden.
    Aslam, Tehseen
    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.
    Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 467-472Conference paper (Refereed)
    Abstract [en]

    Analyzing networks of supply-chains, where each chain is comprised of several actors with different purposes and performance measures, is a difficult task. There exists a large potential in optimizing supply-chains for many companies and therefore the supply-chain optimization problem is of great interest to study. To be able to optimize the supply-chain on a global scale, fast models are needed to reduce computational time. Previous research has been made into the aggregation of factories, but the technique has not been tested against supply-chain problems. When evaluating the configuration of factories and their inter-transportation on a global scale, new insights can be gained about which parameters are important and how the aggregation fits to a supply-chain problem. The paper presents an interactive proof-of-concept model enabling testing of supply chain concepts by users and decision makers.

  • 6.
    Lidberg, Simon
    et al.
    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.
    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.
    Optimizing real-world factory flows using aggregated discrete event simulation modelling: Creating decision-support through simulation-based optimization and knowledge-extraction2019In: Flexible Services and Manufacturing Journal, ISSN 1936-6582, E-ISSN 1936-6590Article in journal (Refereed)
    Abstract [en]

    Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition. 

  • 7.
    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.

  • 8.
    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.

  • 9.
    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.

  • 10.
    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)
  • 11.
    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.

  • 12.
    Pehrsson, Leif
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Manufacturing management and decision support using simulation-based multi-objective optimisation2013Doctoral thesis, monograph (Other academic)
    Abstract [en]

    A majority of the established automotive manufacturers are under severe competitive pressure and their long term economic sustainability is threatened. In particular the transformation towards more CO2-efficient energy sources is a huge financial burden for an already investment capital intensive industry. In addition existing operations urgently need rapid improvement and even more critical is the development of highly productive, efficient and sustainable manufacturing solutions for new and updated products. Simultaneously, a number of severe drawbacks with current improvement methods for industrial production systems have been identified. In summary, variation is not considered sufficient with current analysis methods, tools used are insufficient for revealing enough knowledge to support decisions, procedures for finding optimal solutions are not considered, and information about bottlenecks is often required, but no accurate methods for the identification of bottlenecks are used in practice, because they do not normally generate any improvement actions. Current methods follow a trial-and-error pattern instead of a proactive approach. Decisions are often made directly on the basis of raw static historical data without an awareness of optimal alternatives and their effects. These issues could most likely lead to inadequate production solutions, low effectiveness, and high costs, resulting in poor competitiveness. In order to address the shortcomings of existing methods, a methodology and framework for manufacturing management decision support using simulation-based multi-objective optimisation is proposed. The framework incorporates modelling and the optimisation of production systems, costs, and sustainability. Decision support is created through the extraction of knowledge from optimised data. A novel method and algorithm for the detection of constraints and bottlenecks is proposed as part of the framework. This enables optimal improvement activities with ranking in order of importance can be sought. The new method can achieve a higher improvement rate, when applied to industrial improvement situations, compared to the well-established shifting bottleneck technique. A number of “laboratory” experiments and real-world industrial applications have been conducted in order to explore, develop, and verify the proposed framework. The identified gaps can be addressed with the proposed methodology. By using simulation-based methods, stochastic behaviour and variability is taken into account and knowledge for the creation of decision support is gathered through post-optimality analysis. Several conflicting objectives can be considered simultaneously through the application of multi-objective optimisation, while objectives related to running cost, investments and other sustainability parameters can be included through the use of the new cost and sustainability models introduced. Experiments and tests have been undertaken and have shown that the proposed framework can assist the creation of manufacturing management decision support and that such a methodology can contribute significantly to regaining profitability when applied within the automotive industry. It can be concluded that a proof-of-concept has been rigorously established for the application of the proposed framework on real-world industrial decision-making, in a manufacturing management context.

  • 13.
    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.

  • 14.
    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)
  • 15.
    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.

  • 16.
    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.

  • 17.
    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)
  • 18.
    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.

     

  • 19.
    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.

  • 20.
    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.

  • 21.
    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.

1 - 21 of 21
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