| 1. |
- Barrera Diaz, Carlos Alberto, 1987-, et al.
(författare)
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A Study of Discrete Event Simulation Project Data and Provenance Information Management in an Automotive Manufacturing Plant
- 2017
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Ingår i: Proceedings of the 2017 Winter Simulation Conference. - : IEEE. - 9781538634288 - 9781538634295 - 9781538634301 ; , s. 4012-4023
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Konferensbidrag (refereegranskat)abstract
- Discrete Event Simulation (DES) project data management is a complex and important engineering activity which impacts on an organization’s efficiency. This efficiency could be decreased by the lack of provenance information or the unreliability of existing information regarding previous simulation projects, all of which complicates the reusability of the existing data. This study presents an analysis of the management of simulation projects and their provenance data, according to the different types of scenarios usually found at a manufacturing plant. A survey based on simulation projects at an automotive manufacturing plant was conducted, in order to categorize the information regarding the studied projects, map the available provenance data and standardize its management. This study also introduces an approach that demonstrates how a structured framework based on the specific data involved in the different types of scenarios could allow an improvement of the management of DES projects.
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| 2. |
- Lidberg, Simon, MSc. 1986-, et al.
(författare)
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A Knowledge Extraction Platform for Reproducible Decision-Support from Multi-Objective Optimization Data
- 2022
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Ingår i: SPS2022. - Amsterdam; Berlin; Washington, DC : IOS Press. - 9781643682686 - 9781643682693 ; 21, s. 725-736
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Konferensbidrag (refereegranskat)abstract
- Simulation and optimization enables companies to take decision based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, it can be difficult to visualize and extract knowledge from the large amounts of data generated by a many-objective optimization genetic algorithm, especially with conflicting objectives. Existing tools offer capabilities for extracting knowledge in the form of clusters, rules, and connections. Although powerful, most existing software is proprietary and is therefore difficult to obtain, modify, and deploy, as well as for facilitating a reproducible workflow. We propose an open-source web-based application using commonly available packages in the R programming language to extract knowledge from data generated from simulation-based optimization. This application is then verified by replicating the experimental methodology of a peer-reviewed paper on knowledge extraction. Finally, further work is also discussed, focusing on method improvements and reproducible results.
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| 3. |
- Lidberg, Simon, MSc. 1986-, et al.
(författare)
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A System Architecture for Continuous Manufacturing Decision Support Using Knowledge Generated from Multi-Level Simulation-Based Optimization
- 2024
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Ingår i: Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning. - : IOS Press. - 9781643685106 - 9781643685113 ; , s. 231-243
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Konferensbidrag (refereegranskat)abstract
- Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions.
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| 4. |
- Lidberg, Simon, MSc. 1986-
(författare)
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Aggregated Models Enabling Optimization of Production Networks : Evaluating fast and efficient techniques for aggregation of detailed model data
- 2018
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Rapport (populärvet., debatt m.m.)abstract
- Obtaining data-based decision support faster is a competitive advantage for companies. Faster decisions means that companies can capitalize on opportunities and avert costly mistakes. Simulation as a predictive tool, with the rise of digitalization, is used across many disciplines in the manufacturing industry. When analyzing current and future production lines, more companies are using discrete event simulation software which offers improved results and accuracy compared to static analysis tools. If simulation is coupled with multi-objective optimization and knowledge extraction, new possibilities for production systems are introduced where artificial intelligence can be used to improve the systems. Seeking predictive answers on the manufacturing network level by re-using line models is problematic. Simulation models on the line level are usually detailed to answer specific questions about that production line. Trying to connect several of these models with the intent to optimize the complete manufacturing network will create computationally expensive models. Reducing the complexity of the line models is not enough, a new representation of the line models is needed. New methods for the aggregation of detailed line model data into a faster and more computationally efficient line modules will enable analysis and optimization of manufacturing networks.A novel method for the aggregation of detailed simulation model data to a more efficient meta-model is one part of the expected result for this project. Increasing the generalizability of the method is critical and application studies will be performed on different types of manufacturing processes. After the generalizability has been verified, extending the method to also incorporate the possibility of optimization and knowledge extraction, together with which input data is required, a framework can be created. This aggregation framework will be the main contribution from this project.
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| 5. |
- Lidberg, Simon, 1986-, et al.
(författare)
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Applying Aggregated Line Modeling Techniques to Optimize Real World Manufacturing Systems
- 2018
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Ingår i: Procedia Manufacturing. - : Elsevier. - 2351-9789. ; 25, s. 89-96
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Tidskriftsartikel (refereegranskat)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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| 6. |
- Lidberg, Simon, MSc. 1986-
(författare)
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Evaluating Fast and Efficient Modeling Methods for Simulation-Based Optimization
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As the industry in general, and the automotive industry in particular, is transforming -- due to new technologies and changes in market demands through electrification, digitalization, and globalization -- maintaining a competitive edge will require better predictions. Better predictions of production performance allows companies to capitalize on opportunities, avoid costly mistakes, and be proactive about change.A commonly used tool in manufacturing for the prediction of production performance is discrete-event simulation. In combination with artificial intelligence methods such as multi-objective optimization, in literature often referred to as simulation-based optimization, and knowledge extraction, bottlenecks in the production process can be identified and recipes for optimal improvement order can be obtained. These recipes support the decision-maker in both understanding the production system and improving it optimally in terms of resource efficiency and investment cost. Even though the use of simulation-based optimization is widespread on the production line level, use on the factory level is more scarce. Improvements on the production line level, without a holistic view of factory performance, can be suboptimal and may only lead to increased storage levels instead of increased output to the customer.The main obstacle for applying simulation-based optimization to the factory level is the complexity of its constituent parts, i.e., detailed production line models. Connecting several detailed production line models to create a factory model results in an overly complicated, albeit, accurate model. A single factory model running for one minute would equate to almost 140 days required for an optimization project, too long to provide decision-support relevant to manufacturing decision-making. This can be mitigated by parallel computing, but a more effective approach is to simplify the production line models to decrease the runtime while trying to maintain accuracy. Model simplification methods are approaches to reduce model complexity in new and existing simulation models. Previous research has provided an accurate and runtime efficient simplification method by use of a generic model structure built by common modeling components. Although the method seems promising in a few publications, it was lacking external and internal validity.This project presents simulation-based optimization on the factory level enabled by a model simplification method. By following the design science research methodology, several case-studies mainly in the automotive industry identify issues with the current implementation, propose additions to the method, and validates them.
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| 7. |
- Lidberg, Simon, 1986-, et al.
(författare)
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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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Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 467-472
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Konferensbidrag (refereegranskat)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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| 8. |
- Lidberg, Simon, MSc. 1986-, et al.
(författare)
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Model Simplification Methods for Coded Discrete-Event Simulation Models : A Systematic Review and Experimental Study
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In an increasingly competitive market due to customer demands of customization and an increasing rate of new product variant introductions, companies need to explore new tools to support them to better predict and optimally re-configure their production networks. In terms of the factory flow level, discrete-event simulation and simulation-based optimization represent such kinds of tools available at the disposal of production engineers or managers. For a complex factory consisting of multiple production lines, creating detailed simulation models of these lines and connecting them together can be used for optimization, but the computational complexity can be prohibitively large for acquiring results in time. Model simplification methods can be utilized to reduce the computational complexity of a model. In this study, a systematic literature review is conducted with the aim of identifying simplification methods for coded models, characteristics of the detailed model, type of industry, motivation, and validation measures. Based on the results of the literature review an experimental study wherein the limits of a specific simplification method are analyzed. We compare the output of a dynamically created model with the output of a simplified representation. A correlation can be observed between outputs for medium to large lines, but for smaller lines, there is a larger discrepancy. The simplification method allows for the reduction in simulation runtime, enabling simulation-based optimization of large lines or interconnected simplified models forming a production network, i.e., a factory, to be optimized and analyzed more efficiently, leading to competitive advantages for companies.
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| 9. |
- Lidberg, Simon, MSc. 1986-, et al.
(författare)
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Multi-Level Optimization with Aggregated Discrete-Event Models
- 2020
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Ingår i: Proceedings of the 2020 Winter Simulation Conference. - : IEEE. - 9781728194998 - 9781728195001 ; , s. 1515-1526
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Konferensbidrag (refereegranskat)abstract
- Removing bottlenecks that restrain the overall performance of a factory can give companies a competitive edge. Although in principle, it is possible to connect multiple detailed discrete-event simulation models to form a complete factory model, it could be too computationally expensive, especially if the connected models are used for simulation-based optimizations. Observing that computational speed of running a simulation model can be significantly reduced by aggregating multiple line-level models into an aggregated factory level, this paper investigates, with some loss of detail, if the identified bottleneck information from an aggregated factory model, in terms of which parameters to improve, would be useful and accurate enough when compared to the bottleneck information obtained with some detailed connected line-level models. The results from a real-world, multi-level industrial application study have demonstrated the feasibility of this approach, showing that the aggregation method can represent the underlying detailed line-level model for bottleneck analysis.
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| 10. |
- Lidberg, Simon, 1986-, et al.
(författare)
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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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Ingår i: Flexible Services and Manufacturing Journal. - : Springer. - 1936-6582 .- 1936-6590. ; 32:4, s. 888-912
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Tidskriftsartikel (refereegranskat)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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