| 1. |
- 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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| 2. |
- Goienetxea Uriarte, Ainhoa, 1983-
(författare)
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Bringing Together Lean, Simulation and Optimization : Defining a framework to support decision-making in system design and improvement
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rapid changes in the market including globalization, the requirement for personalizedproducts and services by the customers, shorter product life-cycles, the exponential growthof technological advances, and the demographical changes, will demand organizations toeffectively improve and design their systems in order to survive. This is the actual paradigmcharacterizing the industrial and service sectors. This scenario presents a considerablechallenge to decision makers who will need to decide about how to design and improve amore than ever complex system without compromising the quality of the decision taken.Lean, being a widely applied management philosophy with very powerful principles, itsmethods and tools are static in nature and have some limitations when it comes to the designand improvement of complex and dynamic systems. Some authors have proposed thecombined use of simulation with Lean in order to overcome these limitations. Furthermore,optimization and post-optimization tools coupled to simulation, provide knowledge aboutoptimal or nearly optimal system configurations to choose from. However, even if Leanprinciples, methods and tools, as well as simulation and optimization, pursue the objectiveof supporting organizations regarding system design and improvement, a bilateral approachfor their combination and its benefits have barely been addressed in the literature.Many studies focus only on how specific Lean tools and simulation can be combined, treatingLean purely as a toolbox and not considering how Lean can support the simulation process.The aim of this research is to address this knowledge gap by analyzing the mutualbenefits and presenting a framework for combining Lean, simulation and optimization tobetter support decision makers in system design and improvement where the limitationsof Lean tools and simulation are overcome by their combination. This framework includesa conceptual framework explaining the relationships between the Lean philosophy, methodsand tools with simulation and optimization; the purposes for this combination and stepby step processes to achieve these purposes; the identification of the roles involved in eachprocess; a maturity model providing guidelines on how to implement the framework; existingbarriers for the implementation; and ethical considerations to take into account. Anindustrial handbook has also been written which explains how to deploy the framework.The research has been conducted in three main stages including an analysis of the literatureand the real-world needs, the definition and formulation of the framework, and finally, itsevaluation in real-world projects and with subject matter experts. The main contributionof this research is the reflection provided on the bilateral benefits of the combination, aswell as the defined and evaluated framework, which will support decision makers take qualitydecisions in system design and improvement even in complex scenarios.
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| 3. |
- 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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| 4. |
- Amouzgar, Kaveh, 1980-
(författare)
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Metamodel Based Multi-Objective Optimization with Finite-Element Applications
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As a result of the increase in accessibility of computational resources and the increase of computer power during the last two decades, designers are able to create computer models to simulate the behavior of complex products. To address global competitiveness, companies are forced to optimize the design of their products and production processes. Optimizing the design and production very often need several runs of computationally expensive simulation models. Therefore, integrating metamodels, as an efficient and sufficiently accurate approximate of the simulation model, with optimization algorithms is necessary. Furthermore, in most of engineering problems, more than one objective function has to be optimized, leading to multi-objective optimization(MOO). However, the urge to employ metamodels in MOO, i.e., metamodel based MOO (MB-MOO), is more substantial.Radial basis functions (RBF) is one of the most popular metamodeling methods. In this thesis, a new approach to constructing RBF with the bias to beset a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF and four other well-known metamodeling methods, in terms of accuracy, efficiency and, most importantly, suitability for integration with MOO evolutionary algorithms. It has been found that the proposed approach is accurate in most of the test functions, and it was the fastest compared to other methods. Additionally, the new approach is the most suitable method for MB-MOO, when integrated with evolutionary algorithms. The proposed approach is integrated with the strength Pareto evolutionary algorithm (SPEA2) and applied to two real-world engineering problems: MB-MOO of the disk brake system of a heavy truck, and the metal cutting process in a turning operation. Thereafter, the Pareto-optimal fronts are obtained and the results are presented. The MB-MOO in both case studies has been found to be an efficient and effective method. To validate the results of the latter MB-MOO case study, a framework for automated finite element (FE) simulation based MOO (SB-MOO) of machining processes is developed and presented by applying it to the same metal cutting process in a turning operation. It has been proved that the framework is effective in achieving the MOO of machining processes based on actual FE simulations.
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| 5. |
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Special Issue : Digital Transformation Towards a Sustainable Human Centric and Resilient Production
- 2023
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- The realisation of a successful product requires collaboration between developers andproducers, taking account of stakeholder value, reinforcing the contribution of industry tosociety and enhancing the wellbeing of workers while respecting planetary boundaries.Founded in 2006, the Swedish Production Academy (SPA) aims to drive and developproduction research and education and to increase cooperation within the production area.SPA initiated and hosts the conference Swedish Production Symposium. This specialissue is based on invited papers from the 10th Swedish Production Symposium(SPS2022), held in Skövde, Sweden, from 26–29 April 2022. The overall theme forSPS2022 was ‘Industry 5.0 transformation – towards a sustainable, human-centric, andresilient production’.As stated by the European Commission the vision of Industry 5.0 recognises societalgoals. It goes beyond a techno-economic vision, industrial value chains and growthaiming for the industry to become a resilient provider of prosperity, respecting ourplanets boundaries, and placing the industrial worker, her well-being, at the centre of theproduction process.In this special issue, we set out to explore the transition to a resilient, sustainable andhuman centric industry. The first paper explores the need for a joint strategical vision thatinclude technology (selection, development, and implementation), organisation(structure, agility, management, stakeholder collaborations, work environment) andpeople (skills and competences, participation, innovation and creative collaborativeculture, and change readiness), to achieve a resilient and sustainable production systemeffectively and efficiently. The second paper discusses how reconfigurable manufacturingsystems can enable sustainable manufacturing and circularity, achieving highresponsiveness and cost efficiency. The third paper, a synthesis of universal workplacedesign in assembly, explores how human assembly workplaces can be designed in abetter way in regard to inclusion of diverse worker populations. The fourth paperdiscusses different meanings of digital transformation in manufacturing industry fromboth a theoretical and industrial perspective. The fifth paper explores challenges to designa product service system at an SME as an approach to support transition to Industry 5.0.The concluding paper in this special issue discusses a knowledge extraction platform forreproducible decision support based on data from multi-objective experiments.The organiser of SPS2022 has found these six outstanding papers to perfectly alignwith the theme ‘Industry 5.0 transformation’ and express their gratitude to theEditor-in-Chief of IJMR for accepting them for publication in this special issue.
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| 6. |
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SPS2022 : Proceedings of the 10th Swedish Production Symposium
- 2022
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Proceedings (redaktörskap) (refereegranskat)abstract
- The realization of a successful product requires collaboration between developers and producers, taking account of stakeholder value, reinforcing the contribution of industry to society and enhancing the wellbeing of workers while respecting planetary boundaries. Founded in 2006, the Swedish Production Academy (SPA) aims to drive and develop production research and education and to increase cooperation within the production area.This book presents the proceedings of the 10th Swedish Production Symposium (SPS2022), held in Skövde, Sweden, from 26-29 April 2022. The overall theme of the symposium was ‘Industry 5.0 Transformation – Towards a Sustainable, Human-Centric, and Resilient Production’. Since its inception in 2007, the purpose of SPS has been to facilitate an event at which members and interested participants from industry and academia can meet to exchange ideas. The 69 papers accepted for presentation here are grouped into ten sections: resource-efficient production; flexible production; humans in the production system; circular production systems and maintenance; integrated product and production development; industrial optimization and decision-making; cyber-physical production systems and digital twins; innovative production processes and additive manufacturing; smart and resilient supply chains; and linking research and education. Also included are three sections covering the Special Sessions at SPS2022: artificial intelligence and industrial analytics in industry 4.0; development of resilient and sustainable production systems; and boundary crossing and boundary objects in product and production development.The book will be of interest to all those involved in the development and production of future products.
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