| 1. |
- Andersson, Martin, 1981-, et al.
(författare)
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On the Trade-off Between Runtime and Evaluation Efficiency In Evolutionary Algorithms
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Evolutionary optimization algorithms typically use one or more parameters that control their behavior. These parameters, which are often kept constant, can be tuned to improve the performance of the algorithm on specific problems. However, past studies have indicated that the performance can be further improved by adapting the parameters during runtime. A limitation of these studies is that they only control, at most, a few parameters, thereby missing potentially beneficial interactions between them. Instead of finding a direct control mechanism, the novel approach in this paper is to use different parameter sets in different stages of an optimization. These multiple parameter sets, which remain static within each stage, are tuned through extensive bi-level optimization experiments that approximate the optimal adaptation of the parameters. The algorithmic performance obtained with tuned multiple parameter sets is compared against that obtained with a single parameter set. For the experiments in this paper, the parameters of NSGAII are tuned when applied to the ZDT, DTLZ and WFG test problems. The results show that using multiple parameter sets can significantly increase the performance over a single parameter set.
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| 2. |
- Bernedixen, Jacob
(författare)
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Automated Bottleneck Analysis of Production Systems : Increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Manufacturing companies constantly need to explore new management strategies and new methods to increase the efficiency of their production systems and retain their competitiveness. It is of paramount importance to develop new bottleneck analysis methods that can identify the factors that impede the overall performance of their productionsystems so that the optimal improvement actions can be performed. Many of the bottleneck-related research methods developed in the last two decades are aimed mainly at detecting bottlenecks. Due to their sole reliance on historical data and lackof any predictive capability, they are less useful for evaluating the effect of bottleneck improvements.There is an urgent need for an efficient and accurate method of pinpointing bottlenecks, identifying the correct improvement actions and the order in which these should be carried out, and evaluating their effects on the overall system performance. SCORE (simulation-based constraint removal) is a novel method that uses simulation based multi-objective optimization to analyze bottlenecks. By innovatively formulating bottleneck analysis as a multi-objective optimization problem and using simulation to evaluate the effects of various combinations of improvements, all attainable, maximum throughput levels of the production system can be sought through a single optimization run. Additionally, post-optimality frequency analysis of the Pareto-optimal solutions can generate a rank order of the attributes of the resources required to achieve the target throughput levels. However, in its original compilation, SCORE has a very high computational cost, especially when the simulation model is complex with a large number of decision variables. Some tedious manual setup of the simulation based optimization is also needed, which restricts its applicability within industry, despite its huge potential. Furthermore, the accuracy of SCORE in terms of convergence in optimization theory and correctness of identifying the optimal improvement actions has not been evaluated scientifically.Building on previous SCORE research, the aim of this work is to develop an effective method of automated, accurate bottleneck identification and improvement analysis that can be applied in industry.The contributions of this thesis work include:(1) implementation of a versatile representation in terms of multiple-choice set variables and a corresponding constraint repair strategy into evolutionary multi-objective optimization algorithms;(2) introduction of a novel technique that combines variable screening enabled initializationof population and variable-wise genetic operators to support a more efficient search process;(3) development of an automated setup for SCORE to avoid the tedious manual creation of optimization variables and objectives;(4) the use of ranking distance metrics to quantify and visualize the convergence and accuracy of the bottleneck ranking generated by SCORE.All these contributions have been demonstrated and evaluated through extensive experiments on scalable benchmark simulation models as well as several large-scale simulation models for real-world improvement projects in the automotive industry.The promising results have proved that, when augmented with the techniques proposed in this thesis, the SCORE method can offer real benefits to manufacturing companies by optimizing their production systems.
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| 3. |
- Bernedixen, Jacob, et al.
(författare)
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Multiple Choice Sets and Manhattan Distance Based Equality Constraint Handling for Production Systems Optimization
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Many simulation-based optimization packages provide powerful algorithms to solve industrialproblems. But most of them fail to oer their users the techniques they needto eectively handle multiple-choice problems involving a large set of decision variableswith mixed types (continuous, discrete and combinatorial) and problems that are highlyconstrained (e.g., with many equality constraints). Yet such issues are found in manyreal-world production system design and improvement problems. Thus, this paper introducesa method to eectively embed multiple choice sets and Manhattan-distancebasedconstraint handling into multi-objective optimization algorithms like NSGA-II andNSGA-III. This paper illustrates and evaluates how these two techniques have been appliedtogether to solve optimal workload, buer and workforce allocation problems. Anexample follows, showing their application to a complex production system improvementproblem at an automotive manufacturer.
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| 4. |
- Bernedixen, Jacob, et al.
(författare)
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On the convergence of stochastic simulation-based multi-objective optimization for bottleneck identification
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- By innovatively formulating a bottleneck identication problem into a bi-objective optimization,simulation-based multi-objective optimization (SMO) can be eectively used as a new method for gen-eral production systems improvement. In a single optimization run, all attainable, maximum throughputlevels of the system can be sought through various optimal combinations of improvement changes ofthe resources. Additionally, the post-optimality frequency analysis on the Pareto-optimal solutions cangenerate a rank order of the attributes of the resources required to achieve the target throughput levels.Observing that existing research mainly put emphasis on measuring the convergence of the optimizationin the objective space, leaving no information on when the solutions in the decision space have convergedand stabilized, this paper represents the rst eort in increasing the knowledge about the convergence ofSMO for the rank ordering in the context of bottleneck analysis. By customizing the Spearman's footruledistance and Kendall's tau, this paper presents how these metrics can be used eectively to provide thedesired visual aid in determining the convergence of bottleneck ranking, hence can assist the user todetermine correctly the terminating condition of the optimization process. It illustrates and evaluatesthe convergence of the SMO for bottleneck analysis on a set of scalable benchmark models as well as twoindustrial simulation models. The results have shed promising direction of applying these new metrics tocomplex, real-world applications.
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| 5. |
- Bernedixen, Jacob, et al.
(författare)
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Optimal Buffer Allocation for Semi-synchronized Automotive Assembly Lines using Simulation-based Multi-objective Optimization
- 2011
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Ingår i: Proceedings of the 9th Industrial Simulation Conference. - : Eurosis. - 9789077381632 ; , s. 129-135
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Konferensbidrag (refereegranskat)abstract
- A practical question in industry in designing or re-designing a production system is: how small can intermediated buffers be to ensure the desired production rate? This topic is usually called optimal buffer allocation as the goal is to allocate the minimum buffer capacities to optimize the performance of the line. This paper presents a case study of using simulation-based evolutionary multi-objective optimization to determine the optimal buffer capacities and positions in the reconfiguration of a real-world truck axle assembly line in an automobile manufacturer. The case study has not only revealed the applicability of the methodology in seeking optimal configurations in a truly multi-objective context, it also illustrates how additional important knowledge was gained by analyzing the optimization results in the objective space.
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| 6. |
- Bernedixen, Jacob, et al.
(författare)
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Practical Production Systems Optimization Using Multiple-Choice Sets and Manhattan Distance based Constraints Handling
- 2014
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Ingår i: 12th International Industrial Simulation Conference 2014. - : Eurosis. - 9789077381830 ; , s. 97-103
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Konferensbidrag (refereegranskat)abstract
- Many simulation-based optimization packages provide powerful algorithms to solve large-scale system problems. But most of them fall short to offer their users the techniques to effectively handle decision variables that are of multiple-choice type, as well as equality constraints, which can be found in many real-world industrial system design and improvement problems. Hence, this paper introduces how multiple choice sets and Manhattan-distance-based constraint handling can be effectively embedded into a meta-heuristic algorithm for simulation-based optimization. How these two techniques have been applied together to make the improvement of a complex production system, provided by an automotive manufacturer, possible will also be presented.
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| 7. |
- Bernedixen, Jacob, et al.
(författare)
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Simulation-based multi-objective bottleneck improvement : Towards an automated toolset for industry
- 2015
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Ingår i: Proceedings of the 2015 Winter Simulation Conference. - Press Piscataway, NJ : IEEE Press. - 9781467397438 ; , s. 2183-2194
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Bernedixen, Jacob, et al.
(författare)
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Variables Screening Enabled Multi-Objective Optimization for Bottleneck Analysis of Production Systems
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Bottleneck analysis can be defined as the process that includes both bottleneck identification and improvement. In the literature most of the proposed bottleneck-related methods address mainly bottleneck detection. By innovatively formulating a bottleneck analysis into a bi-objective optimization method, recent research has shown that all attainable, maximized TH of a production system, through various combinations of improvement changes of the resources, can be sought in a single optimization run. Nevertheless, when applied to simulation-based evaluation, such a bi-objective optimization is computationally expensive especially when the simulation model is complex and/or with a large amount of decision variables representing the improvement actions. The aim of this paper is therefore to introduce a novel variables screening enabled bi-objective optimization that is customized for bottleneck analysis of production systems. By using the Sequential Bifurcation screening technique which is particularly suitable for large-scale simulation models, fewer simulation runs are required to find the most influenacing factors in a simulation model. With the knowledge of these input variables, the bi-objective optimization used in the bottleneck analysis can customize the genetic operators on these variables individually according to their rank of main effects with the target to speed up the entire optimization process. The screening-enabled algorithm is then applied to a set of experiments designed to evaluate how well it performs when the number of variables increases is a scalable, benchmark model, as well as two real-world industrial-scale simulation models found in the automotive industry. The results have illustrated the promising direction of incorporating the knowledge of influencing variables and variable-wise genetic operators into a multi-objective optimization algorithm for bottleneck analysis.
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| 9. |
- Karlsson, Ingemar, et al.
(författare)
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Combining augmented reality and simulation-based optimization for decision support in manufacturing
- 2017
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Ingår i: Proceedings of the 2017 Winter Simulation Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538634288 - 9781538634295 - 9781538634301 ; , s. 3988-3999
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 10. |
- Ng, Amos, et al.
(författare)
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Factory flow design and analysis using internet-enabled simulation-based optimization and automatic model generation
- 2011
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Ingår i: Proceedings of the 2011 Winter Simulation Conference. - : IEEE conference proceedings. - 9781457721090 - 9781457721083 ; , s. 2176-2188
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Konferensbidrag (refereegranskat)abstract
- Despite simulation offers tremendous promise for designing and analyzing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conceptual phase of factory flow design. If simulation is used today for system design, it is more often used in later phases when important design decisions have already been made and costs are locked. With an aim to advocate the use of simulation in early phases of factory design and analysis, this paper introduces FACTS Analyzer, a toolset developed based on the concept of integrating model abstraction, automatic model generation and simulation-based optimization under an innovative Internet-based platform. Specifically, it addresses a novel model aggregation and generation method, which when combined together with other system components, like optimization engines, can synthetically enable simulation to become much easier to use and speed up the time-consuming model building, experimentation and optimization processes, in order to support optimal decision making.
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