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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.
    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.

  • 5.
    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)
  • 6.
    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.

  • 7.
    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)
  • 8.
    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)
  • 9.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    Using Soft Computing Techniques in the Simulation-Based Optimisation of Postens Mail Distribution2007In: Proceedings of the 24th Annual  Workshop of the Swedish Artificial Intelligence Society (SAIS 2007), University College of Borås , 2007, p. 193-197Conference paper (Refereed)
  • 10.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    A Metamodel-Assisted Steady-State Evolution Strategy for Simulation-Based Optimization2007In: Trends in Intelligent Systems and Computer Engineering / [ed] Oscar Castillo, Li Xu, Sio-Iong Ao, Springer Science+Business Media B.V., 2007, p. 1-13Chapter in book (Refereed)
  • 11.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Ng, Amos
    A Surrogate-Assisted Steady-State Evolution Strategy with a New Offspring Selection Procedure2007In: The 24th annual workshop of the Swedish Artificial Intelligence Society (SAIS2007), Borås: University College of Borås , 2007, p. 185-189Conference paper (Refereed)
  • 12.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Metamodel-assisted Global Search Using a Probing Technique2007In: The IAENG International Conference on Artificial Intelligence and Applications (ICAIA'07), International Association of Engineers, 2007, p. 83-88Conference paper (Refereed)
    Abstract [en]

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

  • 13.
    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.
    Simulation-Based Optimisation Using Global Search and Neural Network Metamodels2006In: Modelling and simulation, Eurosis, Ghent University , 2006, p. 182-186Conference paper (Refereed)
  • 14.
    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.
    Simulation-based optimisation using local search and neural network metamodels2006In: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006 / [ed] Angel Pasqual del Pobil, Anaheim: ACTA Press, 2006, p. 178-183Conference paper (Refereed)
    Abstract [en]

    This paper presents a new algorithm for enhancing the efficiency of simulation-based optimisation using local search and neural network metamodels. The local search strategy is based on steepest ascent Hill Climbing. In contrast to many other approaches that use a metamodel for simulation optimisation, this algorithm alternates between the metamodel and its underlying simulation model, rather than using them sequentially. On-line learning of the metamodel is applied to improve its accuracy in the current region of the search space. The proposed algorithm is applied to a theoretical benchmark problem as well as a real-world manufacturing optimisation problem and initial results show good performance when compared to a standard Hill Climbing strategy.

  • 15.
    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)
  • 16.
    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.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    A Case Study of Using Simulation and Soft Computing Techniques for Optimisation of Manufacturing Systems2007In: Proceedings of Swedish Production Symposium 2007, Gothenburg, Sweden, August 28-30, 2007, 2007Conference paper (Refereed)
  • 17.
    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.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    Simulation-Based Optimization of a Complex Mail Transportation Network2006Conference paper (Refereed)
  • 18.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Lezama, Thomas
    University of Skövde, School of Technology and Society.
    Ekberg, Jonas
    Falk, Stephan
    Stablum, Peter
    Simulation-Based Multi-Objective Optimization of a Real-World Operation Scheduling Problem2006In: WSC '06 Proceedings of the 38th conference on Winter simulation, Winter Simulation Conference , 2006, p. 1757-1764Conference paper (Refereed)
    Abstract [en]

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

  • 19.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Grimm, Henrik
    University of Skövde, School of Technology and Society.
    On-line Instrumentation for Simulation-based Optimization2006In: Proceedings of the Winter Simulation Conference, 2006: WSC 06, IEEE Press, 2006, p. 304-311Conference paper (Refereed)
    Abstract [en]

    Traditionally, a simulation-based optimization (SO) system is designed as a black-box in which the internal details of the optimization process is hidden from the user and only the final optimization solutions are presented. As the complexity of the SO systems and the optimization problems to be solved increases, instrumentation - a technique for monitoring and controlling the SO processes - is becoming more important. This paper proposes a white-box approach by advocating the use of instrumentation components in SO systems, based on a component-based architecture. This paper argues that a number of advantages, including efficiency enhancement, gaining insight from the optimization trajectories and higher controllability of the SO processes, can be brought out by an on-line instrumentation approach. This argument is supported by the illustration of an instrumentation component developed for a SO system designed for solving real-world multi-objective operation scheduling problems

  • 20.
    Syberfeldt, Anna
    et al.
    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.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Design of Experiments for Training Metamodels in Simulation-Based Optimisation of Manufacturing Systems2008In: Proceedings of The 18th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM'08), Skövde: University of Skövde , 2008Conference paper (Refereed)
  • 21.
    Syberfeldt, 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.
    Multi-Objective Simulation-Based Optimization of Production Systems with Consideration Noise2008In: / [ed] Bengt Åke Lindberg & Johan Stahre, Stockholm: Swedish Production Academy , 2008Conference paper (Refereed)
    Abstract [en]

    Many production optimization problems approached by simulation are subject to noise.When evolutionary algorithms are applied to such problems, noise during evaluation of solutions adversely affects the evolutionary selection process and the performance of the algorithm. In this paper we present a noise compensation technique that efficiently deals with the negative effects of noisy simulations in multi-objective optimization problems. Basically, this technique uses an iterative re-sampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. The technique is implemented in MOPSA-EA, an existing evolutionary algorithm designed specifically for real-world simulation-optimization problems. In evaluating the new technique, it is applied on a benchmark problem and on two real-world problems of manufacturing optimization. A comparison of the performance of existing algorithms indicates the potential of the proposed technique.

  • 22.
    Syberfeldt, Anna
    et al.
    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.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Andersson, Martin
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Karlsson, Ingemar
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Simulation-Based Optimization of a Complex Mail Transportation Network2008In: Proceedings of the 2008 Winter Simulation Conference / [ed] S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler, New York: IEEE conference proceedings, 2008, p. 2625-2631Conference paper (Refereed)
    Abstract [en]

    The Swedish Postal Services receives and distributes over 22 million pieces of mail every day. Mail transportation takes place overnight by airplanes, trains, trucks, and cars in a transportation network comprising a huge number of possible routes. For testing and analysis of different transport solutions, a discrete-event simulation model of the transportation network has been developed. This paper describes the optimization of transport solutions using evolutionary algorithms coupled with the simulation model. The vast transportation network in combination with a large number of possible transportation configurations and conflicting optimization criteria make the optimization problem very challenging. A large number of simulation evaluations are needed before an acceptable solution is found, making the computational cost of the problem severe. To address this problem, a computationally cheap surrogate model is used to offload the optimization process.

  • 23.
    Syberfeldt, 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.
    John, Robert I.
    Centre for Computational Intelligence, De Montfort University, Leicester, United Kingdom.
    A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems2008In: 2008 IEEE Congress on Evolutionary Computation, CEC 2008, IEEE conference proceedings, 2008, p. 3177-3184Conference paper (Refereed)
  • 24.
    Syberfeldt, Anna
    et al.
    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.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Multi-Objective Evolutionary Simulation-Optimization of a Real-World Manufacturing Problem2008In: Proceedings of The 18th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM'08), Skövde: University of Skövde , 2008Conference paper (Refereed)
1 - 24 of 24
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