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  • 151.
    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)
  • 152.
    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.

  • 153.
    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)
  • 154.
    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)
  • 155.
    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)
  • 156.
    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.

  • 157.
    Persson, Anna
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    OPTIMisation using Intelligent Simulation Tools2006In: Proceedings of SAIS 2006: The 23rd Annual Workshop of the Swedish Artificial Intelligence Society, Swedish Artificial Intelligence Society - SAIS , 2006, p. 83-86Conference paper (Other academic)
    Abstract [en]

    This paper describes the research project OPTIMIST at the University of Skövde. The project is focused on the development and application of intelligent techniques for simulation-based optimisation of real-world industrial problems. A software environment called OPTIMISE which tightly integrates Discrete-Event Simulation systems with AIbased optimisation is developed within the project.

  • 158.
    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

  • 159.
    Pettersson, Lars
    et al.
    University of Skövde, School of Technology and Society.
    Adolfsson, Josef
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    De Vin, Leo J.
    University of Skövde, School of Technology and Society.
    Cell Monitoring and Diagnostics Using Computer Aided Robotics2007In: Proceedings of 40th CIRP International Seminar on Manufacturing Systems, Liverpool UK, 2007, 2007Conference paper (Refereed)
  • 160.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Integration of events and offline measurement data from a population of similar entities for condition monitoringIn: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052Article in journal (Refereed)
    Abstract [en]

    In this paper, an approach for integration of data from different sources and from a population of similar monitored entities is presented with evaluation procedure based on multiple machine learning methods that allows selection of a proper combination of methods for data integration and feature selection. It is exemplified on the real-world case from manufacturing industry with application to double ball-bar measurement from a population of machine tools. Historical data from the period of four years from a population of 29 similar multitask machine tools are analysed. Several feature selection methods are evaluated. Finally, simple economic evaluation is presented with application to proposed condition based approach. With assumed parameters, potential improvement in long term of 6 times reduced amount of unplanned stops and 40% reduced cost has been indicated with respect to optimal time based replacement policy.

  • 161.
    Senington, Richard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Baumeister, Fabian
    University of Skövde, School of Engineering Science.
    Ng, Amos
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Oscarsson, Jan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    A linked data approach for the connection of manufacturing processes with production simulation models2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 70, p. 440-445Article in journal (Refereed)
    Abstract [en]

    This paper discusses the expected benefits of using linked data for the tasks of gathering, managing and understanding the data of smart factories. It has the further specific focus of using this data to maintaining a Digital Twin for the purposes of analysis and optimisation of such factories. The proposals are motivated by the use of an industrial example looking at the types of information required, the variation in data which is available and the requirements of an analysis platform to provide parameters for seamless, automated simulation and optimisation. 

  • 162.
    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.

  • 163.
    Siegmund, Florian
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Karlsson, Alexander
    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.
    Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization of Stochastic Systems2013Conference paper (Refereed)
    Abstract [en]

    In Multi-objective Optimization many solutions have to be evaluated in order to provide the decision maker with a diverse Pareto-front. In Simulation-based Optimization the number of optimization function evaluations is very limited. If preference information is available however, the available function evaluations can be used more effectively by guiding the optimization towards interesting, preferred regions. One such algorithm for guided search is the R-NSGA-II algorithm. It takes reference points provided by the decision maker and guides the optimization towards areas of the Pareto-front close to the reference points.In Simulation-based Optimization the modeled systems are often stochastic and a reliable quality assessment of system configurations by resampling requires many simulation runs. Therefore optimization practitioners make use of dynamic resampling algorithms that distribute the available function evaluations intelligently on the solutions to be evaluated. Criteria for sampling allocation can be a.o. objective value variability, closeness to the Pareto-front indicated by elapsed time, or the dominance relations between different solutions based on distances between objective vectors and their variability.In our work we combine R-NSGA-II with several resampling algorithms based on the above mentioned criteria. Due to the preference information R-NSGA-II has fitness information based on distance to reference points at its disposal. We propose a resampling strategy that allocates more samples to solutions close to a reference point.Previously, we proposed extensions of R-NSGA-II that adapt algorithm parameters like population size, population diversity, or the strength of the Pareto-dominance relation continuously to optimization problem characteristics. We show how resampling algorithms can be integrated with those extensions.The applicability of the proposed algorithms is shown in a case study of an industrial production line for car manufacturing.

  • 164.
    Siegmund, Florian
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Adaptive Guided Evolutionary Multi-Objective Optimization2013Conference paper (Refereed)
    Abstract [en]

    In Multi-objective Optimization many solutions have to be evaluated in order to provide the decision maker with a diverse Pareto-front. In Simulation-based Optimization the number of optimization function evaluations is very limited. If preference information is available however, the available function evaluations can be used more effectively by guiding the optimization towards interesting, preferred regions. One such algorithm for guided search is the Reference-point guided NSGA-II. It takes reference points provided by the decision maker and guides the optimization towards areas of the Pareto-front close to the reference points.We propose several extensions of R-NSGA-II. In the beginning of the optimization runtime the population is spread-out in the objective space while towards the end of the runtime most solutions are close to reference points. The purpose of a large population is to avoid local optima and to explore the search space which is less important when the algorithm has converged to the reference points. Therefore, we reduce the population size towards the end of the runtime. R-NSGA-II controls the objective space diversity through the epsilon parameter. We reduce the diversity in the population as it approaches the reference points. In a previous study we showed that R-NSGA-II keeps a high diversity until late in the optimization run which is caused by the Pareto-fitness. This slows down the progress towards the reference points. We constrain the Pareto-fitness to force a faster convergence. For the same reason an approach is presented that delays the use of the Pareto-fitness: Initially, the fitness is based only on reference point distance and diversity. Later, when the population has converged towards the Pareto-front, Pareto-fitness is considered as primary-, and distance as secondary fitness.

  • 165.
    Siegmund, Florian
    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.
    Deb, Kalyanmoy
    Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India.
    A Comparative Study of Dynamic Resampling Strategies for Guided Evolutionary Multi-Objective Optimization2013In: 2013 IEEE Congress on Evolutionary Computation, CEC 2013, IEEE conference proceedings, 2013, p. 1826-1835Conference paper (Refereed)
    Abstract [en]

    In Evolutionary Multi-objective Optimization many solutions have to be evaluated to provide the decision maker with a diverse choice of solutions along the Pareto-front, in particular for high-dimensional optimization problems. In Simulation-based Optimization the modeled systems are complex and require long simulation times. In addition the evaluated systems are often stochastic and reliable quality assessment of system configurations by resampling requires many simulation runs. As a countermeasure for the required high number of simulation runs caused by multiple optimization objectives the optimization can be focused on interesting parts of the Pareto-front, as it is done by the Reference point-guided NSGA-II algorithm (R-NSGA-II) [9]. The number of evaluations needed for the resampling of solutions can be reduced by intelligent resampling algorithms that allocate just as much sampling budget needed in different situations during the optimization run. In this paper we propose and compare resampling algorithms that support the R-NSGA-II algorithm on optimization problems with stochastic evaluation functions. © 2013 IEEE.

  • 166.
    Siegmund, Florian
    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.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization2017In: Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings / [ed] Heike Trautmann, Rudolph Günter, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, and Christian Grimme, Springer, 2017, Vol. 10173, p. 560-574Conference paper (Refereed)
    Abstract [en]

    In Simulation-based Evolutionary Multi-objective Optimization, the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space with, for example, the Reference Point-based NSGA-II algorithm (R-NSGA-II). Since the Pareto-relation is the primary fitness function in R-NSGA-II, the algorithm focuses on exploring the objective space with high diversity. Only after the population has converged closeto the Pareto-front does the influence of the reference point distance as secondary fitness criterion increase and the algorithm converges towards the preferred area on the Pareto-front.In this paper, we propose a set of extensions of R-NSGA-II which adaptively control the algorithm behavior, in order to converge faster towards the reference point. The adaption can be based on criteria such as elapsed optimization time or the reference point distance, or a combination thereof. In order to evaluate the performance of the adaptive extensions of R-NSGA-II, a performance metric for reference point-based EMO algorithms is used, which is based on the Hypervolume measure called the Focused Hypervolume metric. It measures convergence and diversity of the population in the preferred area around the reference point. The results are evaluated on two benchmark problems ofdifferent complexity and a simplistic production line model.

  • 167.
    Siegmund, Florian
    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.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    A Ranking and Selection Strategy for Preference-based Evolutionary Multi-objective Optimization of Variable-Noise Problems2016In: 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE conference proceedings, 2016, p. 3035-3044Conference paper (Refereed)
    Abstract [en]

    In simulation-based Evolutionary Multi-objective Optimization the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space, for example with the R-NSGA-II algorithm [9], which uses a reference point specified by the decision maker. When stochastic systems are simulated, the uncertainty of the objective values might degrade the optimization performance. By sampling the solutions multiple times this uncertainty can be reduced. However, resampling methods reduce the overall number of evaluated solutions which potentially worsens the optimization result. In this article, a Dynamic Resampling strategy is proposed which identifies the solutions closest to the reference point which guides the population of the Evolutionary Algorithm. We apply a single-objective Ranking and Selection resampling algorithm in the selection step of R-NSGA-II, which considers the stochastic reference point distance and its variance to identify the best solutions. We propose and evaluate different ways to integrate the sampling allocation method into the Evolutionary Algorithm. On the one hand, the Dynamic Resampling algorithm is made adaptive to support the EA selection step, and it is customized to be used in the time-constrained optimization scenario. Furthermore, it is controlled by other resampling criteria, in the same way as other hybrid DR algorithms. On the other hand, R-NSGA-II is modified to rely more on the scalar reference point distance as fitness function. The results are evaluated on a benchmark problem with variable noise landscape.

  • 168.
    Siegmund, Florian
    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.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Dynamic Resampling for Preference-based Evolutionary Multi-Objective Optimization of Stochastic Systems2015Conference paper (Refereed)
    Abstract [en]

    In Multi-objective Optimization many solutions have to be evaluated in order to provide the decision maker with a diverse choice of solutions along the Pareto-front. In Simulation-based Optimization the number of optimization function evaluations is usually very limited due to the long execution times of the simulation models. If preference information is available however, the available number of function evaluations can be used more effectively. The optimization can be performed as a guided, focused search which returns solutions close to interesting, preferred regions of the Pareto-front. One such algorithm for guided search is the Reference-point guided Non-dominated Sorting Genetic Algorithm II, R-NSGA-II. It is a population-based Evolutionary Algorithm that finds a set of non-dominated solutions in a single optimization run. R-NSGA-II takes reference points in the objective space provided by the decision maker and guides the optimization towards areas of the Pareto-front close the reference points.

    In Simulation-based Optimization the modeled and simulated systems are often stochastic and a common method to handle objective noise is Resampling. Reliable quality assessment of system configurations by resampling requires many simulation runs. Therefore, the optimization process can benefit from Dynamic Resampling algorithms that distribute the available function evaluations among the solutions in the best possible way. Solutions can vary in their sampling need. For example, solutions with highly variable objective values have to be sampled more times to reduce their objective value standard error. Dynamic resampling algorithms assign as much samples to them as is needed to reduce the uncertainty about their objective values below a certain threshold. Another criterion the number of samples can be based on is a solution's closeness to the Pareto-front. For solutions that are close to the Pareto-front it is likely that they are member of the final result set. It is therefore important to have accurate knowledge of their objective values available, in order to be able to to tell which solutions are better than others. Usually, the distance to the Pareto-front is not known, but another criterion can be used as an indication for it instead: The elapsed optimization time. A third example of a resampling criterion can be the dominance relations between different solutions. The optimization algorithm has to determine for pairs of solutions which is the better one. Here both distances between objective vectors and the variance of the objective values have to be considered which requires a more advanced resampling technique. This is a Ranking and Selection problem.

    If R-NSGA-II is applied in a scenario with a stochastic fitness function resampling algorithms have to be used to support it in the best way and avoid a performance degradation due to uncertain knowledge about the objective values of solutions. In our work we combine R-NSGA-II with several resampling algorithms that are based on the above mentioned resampling criteria or combinations thereof and evaluate which are the best criteria the sampling allocation can be based on, in which situations.

    Due to the preference information R-NSGA-II has an important fitness information about the solutions at its disposal: The distance to reference points. We propose a resampling strategy that allocates more samples to solutions close to a reference point. This idea is then extended with a resampling technique that compares solutions based on their distance to the reference point. We base this algorithm on a classical Ranking and Selection algorithm, Optimal Computing Budget Allocation, and show how OCBA can be applied to support R-NSGA-II. We show the applicability of the proposed algorithms in a case study of an industrial production line for car manufacturing.

  • 169.
    Siegmund, Florian
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos H. C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Deb, Kalyanmoy
    Department of Mechanical Engineering, Indian Institute of Technology Kanpur, India.
    Finding a preferred diverse set of Pareto-optimal solutions for a limited number of function calls2012In: 2012 IEEE Congress on Evolutionary Computation, IEEE conference proceedings, 2012, p. 2417-2424Conference paper (Refereed)
    Abstract [en]

    Evolutionary Multi-objective Optimization aims at finding a diverse set of Pareto-optimal solutions whereof the decision maker can choose the solution that fits best to her or his preferences. In case of limited time (of function evaluations) for optimization this preference information may be used to speed up the search by making the algorithm focus directly on interesting areas of the objective space. The R-NSGA-II algorothm (1) uses reference points to which the search is guided specified according to the preferences of the user. In this paper, we propose an extension to R-NSGA-II that limits the Pareto-fitness to speed up the search for a limited number of function calls. It avoids to automatically select all solutions of the first front of the candidate set into the next population. In this way non-preferred Pareto-optimal solutions are not considered thereby accelerating the search process. With focusing comes the necessity to maintain diversity. In R-NSGA-II this is achieved with the help of a clustering algorithm which keeps the found solutions above a minimum distance ε. In this paper, we propose a self-adaptive ε approach that autonomously provides the decision maker with a more diverse solution set if the found Pareto-set is situated further away from a reference point. Similarly, the approach also varies the diversity inside of the Pareto-set. This helps the decision maker to get a better overview of the available solutions and supports decisions about how to adapt the reference points.

  • 170.
    Siegmund, Florian
    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.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, East Lansing, USA.
    Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-objective Optimization of Invariant-Noise Problems2016In: Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 – April 1, 2016, Proceedings, Part II / [ed] Giovanni Squillero, Paolo Burelli, 2016, Vol. 9598, p. 311-326Conference paper (Refereed)
    Abstract [en]

    In Simulation-based Evolutionary Multi-objective Optimization (EMO) the available time for optimization usually is limited. Since many real-world optimization problems are stochastic models, the optimization algorithm has to employ a noise compensation technique for the objective values. This article analyzes Dynamic Resampling algorithms for handling the objective noise. Dynamic Resampling improves the objective value accuracy by spending more time to evaluate the solutions multiple times, which tightens the optimization time limit even more. This circumstance can be used to design Dynamic Resampling algorithms with a better sampling allocation strategy that uses the time limit. In our previous work, we investigated Time-based Hybrid Resampling algorithms for Preference-based EMO. In this article, we extend our studies to general EMO which aims to find a converged and diverse set of alternative solutions along the whole Pareto-front of the problem. We focus on problems with an invariant noise level, i.e. a flat noise landscape.

  • 171.
    Siegmund, Florian
    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.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization2015In: Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part I / [ed] António Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello, Springer, 2015, p. 366-380Conference paper (Refereed)
    Abstract [en]

    In Guided Evolutionary Multi-objective Optimization the goal is to find a diverse, but locally focused non-dominated front in a decision maker’s area of interest, as close as possible to the true Pareto-front. The optimization can focus its efforts towards the preferred area and achieve a better result [9, 17, 7, 13]. The modeled and simulated systems are often stochastic and a common method to handle the objective noise is Resampling. The given preference information allows to define better resampling strategies which further improve the optimization result. In this paper, resampling strategies are proposed that base the sampling allocation on multiple factors, and thereby combine multiple resampling strategies proposed by the authors in [15]. These factors are, for example, the Pareto-rank of a solution and its distance to the decision maker’s area of interest. The proposed hybrid Dynamic Resampling Strategy DR2 is evaluated on the Reference point-guided NSGA-II optimization algorithm (R-NSGA-II) [9].

  • 172. Sundberg, M
    et al.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    Distributed modular logic controllers for modular conveyor systems2003In: Proceedings of the 20th International Manufacturing Conference, 2003, p. 493-502Conference paper (Other academic)
  • 173.
    Sundberg, Martin
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Internetstyrd Elektronik2006Other (Other (popular science, discussion, etc.))
  • 174.
    Sundberg, Martin
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Adolfsson, Josef
    University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, School of Technology and Society.
    Simulation Supported Service and Maintenance in Manufacturing2006In: Proceedings of the International Manufacturing Conference, IMC 23, 2006, 2006, p. 559-566Conference paper (Other academic)
  • 175.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Andersson, Martin
    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.
    Bengtsson, Victor
    PostNord AB, Solna, Sweden .
    Multi-Objective Evolutionary Optimization of Personnel Scheduling2015In: International Journal of Artificial Intelligence & Applications, ISSN 0976-2191, E-ISSN 0975-900X, Vol. 6, no 1, p. 41-52Article in journal (Refereed)
    Abstract [en]

    This paper presents an evolutionary multi-objective simulation-optimization system for personnelscheduling. The system is developed for the Swedish postal services and aims at finding personnelschedules that minimizes both total man hours and the administrative burden of the person responsible forhandling schedules. For the optimization, the multi-objective evolutionary algorithm NSGA-II isimplemented. In order to make the optimization fast enough, a two-level parallelisation model is beingadopted. The simulation-optimization system is evaluated on a real-world test case and results from theevaluation shows that the algorithm is successful in optimizing the problem.

  • 176.
    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)
  • 177.
    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.

  • 178.
    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.

  • 179.
    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)
  • 180.
    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)
  • 181.
    Syberfeldt, Anna
    et al.
    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.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    An Industrial Case Study of Web-based Simulation-Optimization2011In: Proceedings of the 9th Industrial Simulation Conference, Eurosis , 2011, p. 115-120Conference paper (Refereed)
    Abstract [en]

    This paper presents a web-based simulation-optimization system for improving production schedules in an advanced manufacturing cell at Volvo Aero Corporation in Sweden. The optimization aims at prioritizing components being processed in the cell in a way that minimizes both tardiness and lead times. Results from evaluating the implemented system shows a great improvement potential, but also indicates that further development is necessary before the system can be taken into operation.

  • 182.
    Syberfeldt, Anna
    et al.
    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.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Svantesson, Joakim
    Volvo Aero Corporation, SE- 461 81 Trollhättan, Sweden.
    Almgren, Torgny
    Volvo Aero Corporation, SE- 461 81 Trollhättan, Sweden.
    A web-based platform for the simulation-optimization of industrial problems2013In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 64, no 4, p. 987-998Article in journal (Refereed)
    Abstract [en]

    This study presents a platform for industrial, real-world simulation-optimization based on web techniques. The design of the platform is intended to be generic and thereby make it possible to apply the platform in various problem domains. In the implementation of the platform, modern web techniques, such as Ajax, JavaScript, GWT, and ProtoBuf, are used. The platform is tested and evaluated on a real industrial problem of production optimization at Volvo Aero Corporation, a company that develops and manufactures high-technology components for aircraft and gas turbine engines. The results of the evaluation show that while the platform has several benefits, implementing a web-based system is not completely straightforward. At the end of the paper, possible pitfalls are discussed and some recommendations for future implementations are outlined. (C) 2013 Elsevier Ltd. All rights reserved.

  • 183.
    Syberfeldt, Anna
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    John, Robert I.
    De Montfort University.
    Moore, Philip
    De Montfort University.
    Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling2010In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 204, no 3, p. 533-544Article in journal (Refereed)
    Abstract [en]

    Many real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques.

  • 184.
    Syberfeldt, Anna
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    John, Robert I.
    Centre for Computational Intelligence, De Montfort University, Leicester, United Kingdom.
    Moore, Philip
    Computing Sciences and Engineering, De Montfort University, Leicester, United Kingdom.
    Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem2009In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 25, no 6, p. 926-931Article in journal (Refereed)
    Abstract [en]

    Many real-world manufacturing problems are too complex to be modelled analytically. For these problems, simulation can be a powerful tool for system analysis and optimisation. While traditional optimisation methods have been unable to cope with the complexities of many problems approached by simulation, evolutionary algorithms have proven to be highly useful. This paper describes how simulation and evolutionary algorithms have been combined to improve a manufacturing cell at Volvo Aero in Sweden. This cell produces high-technology engine components for civilian and military airplanes, and also for space rockets. Results from the study show that by using simulation and evolutionary algorithms, it is possible to increase the overall utilisation of the cell and at the same time decrease the number of overdue components.

  • 185.
    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)
  • 186.
    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)
  • 187.
    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)
  • 188.
    Wang, Lihui
    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.Deb, KalyanmoyUniversity of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Multi-objective Evolutionary Optimisation for Product Design and Manufacturing2011Collection (editor) (Refereed)
1234 151 - 188 of 188
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