| 1. |
- Aslam, Tehseen, 1981-, et al.
(author)
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Towards an industrial testbed for holistic virtual production development
- 2018
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In: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 369-374
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Conference paper (peer-reviewed)abstract
- Virtual production development is adopted by many companies in the production industry and digital models and virtual tools are utilized for strategic, tactical and operational decisions in almost every stage of the value chain. This paper suggest a testbed concept that aims the production industry to adopt a virtual production development process with integrated tool chains that enables holistic optimizations, all the way from the overall supply chain performance down to individual equipment/devices. The testbed, which is fully virtual, provides a mean for development and testing of integrated digital models and virtual tools, including both technical and methodological aspects.
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| 2. |
- Bernedixen, Jacob
(author)
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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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Doctoral thesis (other academic/artistic)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.
(author)
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Simulation-based multi-objective bottleneck improvement : Towards an automated toolset for industry
- 2015
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In: Proceedings of the 2015 Winter Simulation Conference. - Press Piscataway, NJ : IEEE Press. - 9781467397438 ; , s. 2183-2194
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Conference paper (peer-reviewed)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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| 4. |
- Dudas, Catarina, et al.
(author)
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Integration of data mining and multi-objective optimisation for decision support in production system development
- 2014
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In: International journal of computer integrated manufacturing (Print). - : Taylor & Francis. - 0951-192X .- 1362-3052. ; 27:9, s. 824-839
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Journal article (peer-reviewed)abstract
- Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.
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| 5. |
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| 6. |
- Karlsson, Ingemar
(author)
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An interactive decision support system using simulation-based optimization and knowledge extraction
- 2018
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Doctoral thesis (other academic/artistic)abstract
- The use of simulation to improve existing manufacturing systems is not new, but simulation can also be used increase the understanding of production systems that have not yet been built. The power of simulation models can be further enhanced by using simulation-based optimization, in which an optimization algorithm tries to find optimal solutions, given certain objectives. However, extracting knowledge from the data resulting from simulation experiments and simulation-based optimization is a complex task. Therefore, tools are needed to assist users in this task. These tools can be visual, like diagrams, or can be generated by data mining. The process of running a study using simulation-based optimization to extract knowledge is a manual task that can in part be automated using existing tools, but to the author’s knowledge there is no software that implements the complete process. This work aims to develop a novel decision support system to support the generic decision process when using simulation and simulation-based optimization. The first step in setting up such a system is to understand how industry currently uses simulation and simulation-based optimization in manufacturing operations. Thus a questionnaire was distributed to manufacturing companies and organizations. The results showed that these techniques are being used, but that companies want more help with the analysis of the results as well as an automated guide in the decision process. This work proposes a system that supports a generic decision process by providing a tool with which a user can define a workflow in their organization, using simulation-based optimization as one component. The decision support system then provides tools for extracting knowledge in the form of diagrams and performs data mining for automated analysis. Data mining is part of the workflow as a tool for extracting knowledge after an optimization, as well as a tool for guiding optimization to suit the users’ preferences. The decision support system also provides for visualization of simulation models and optimization results using augmented reality. A head-mounted display helps users to see the results and model behaviors in 3D. This technology also makes it possible for users to collaborate, both in the same location and remotely. These visual and automatic analysis tools are shown to be effective in several application studies of real-world production scenarios in which data mining has been used to extract important knowledge that would be hard to obtain manually. Together with the automated workflow and efficient visualization of simulation and optimization results in augmented reality, the decision support system is believed to be an effective tool for extracting knowledge for general production systems design and analysis.
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| 7. |
- Karlsson, Ingemar, et al.
(author)
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Combining augmented reality and simulation-based optimization for decision support in manufacturing
- 2017
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In: Proceedings of the 2017 Winter Simulation Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538634288 - 9781538634295 - 9781538634301 ; , s. 3988-3999
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Conference paper (peer-reviewed)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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| 8. |
- Lidberg, Simon, 1986-, et al.
(author)
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Applying Aggregated Line Modeling Techniques to Optimize Real World Manufacturing Systems
- 2018
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In: Procedia Manufacturing. - : Elsevier. - 2351-9789. ; 25, s. 89-96
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Journal article (peer-reviewed)abstract
- The application of discrete event simulation methodology in the analysis of higher level manufacturing systems has been limited due to model complexity and the lack of aggregation techniques for manufacturing lines. Recent research has introduced new aggregation methods preparing for new approaches in the analysis of higher level manufacturing systems or networks. In this paper one of the new aggregated line modeling techniques is successfully applied on a real world manufacturing system, solving a real-world problem. The results demonstrate that the aggregation technique is adequate to be applied in plant wide models. Furthermore, in this particular case, there is a potential to reduce storage levels by over 25 %, through leveling the production flow, without compromising deliveries to customers.
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| 9. |
- Lidberg, Simon, 1986-, et al.
(author)
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Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model
- 2018
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In: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 467-472
-
Conference paper (peer-reviewed)abstract
- Analyzing networks of supply-chains, where each chain is comprised of several actors with different purposes and performance measures, is a difficult task. There exists a large potential in optimizing supply-chains for many companies and therefore the supply-chain optimization problem is of great interest to study. To be able to optimize the supply-chain on a global scale, fast models are needed to reduce computational time. Previous research has been made into the aggregation of factories, but the technique has not been tested against supply-chain problems. When evaluating the configuration of factories and their inter-transportation on a global scale, new insights can be gained about which parameters are important and how the aggregation fits to a supply-chain problem. The paper presents an interactive proof-of-concept model enabling testing of supply chain concepts by users and decision makers.
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| 10. |
- Lidberg, Simon, 1986-, et al.
(author)
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Optimizing real-world factory flows using aggregated discrete event simulation modelling : Creating decision-support through simulation-based optimization and knowledge-extraction
- 2020
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In: Flexible Services and Manufacturing Journal. - : Springer. - 1936-6582 .- 1936-6590. ; 32:4, s. 888-912
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Journal article (peer-reviewed)abstract
- Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition.
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