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  • 451.
    Rexfelt, Oskar
    et al.
    Chalmers University of Technology.
    Hjort af Ornäs, Viktor
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Consumer acceptance of product-service systems: Designing for relative advantages and uncertainty reductions2009In: Journal of Manufacturing Technology Management, ISSN 1741-038X, E-ISSN 1758-7786, Vol. 20, no 5, p. 674-699Article in journal (Refereed)
    Abstract [en]

    Purpose – Product-service systems (PSS) could potentially benefit consumers, but empirical studies of business-to-consumer PSS solutions have been scarce. The purpose of this paper is to identify conditions for consumer acceptance, and propose a methodology for PSS development. Design/methodology/approach – Factors influencing consumer acceptance of PSS are investigated through focus groups and individual interviews, and elaborated in relation to theory from user acceptance and innovation adoption literature. Procedures for conceptual development of PSS are then proposed, based on methodology adapted from user-centred design. Findings – The two factors “impact on everyday life”, and “uncertainties” in anticipating such consequences were repeatedly brought up by participants. PSS affect consumers through practical implications for the activities they engage in. This goes beyond the service encounter, is highly complex and case specific why development processes should include iterative studies with consumers. Research limitations/implications – The studies use hypothetical PSS offers. Validation and refinement of the proposed methodology would require application in commercial development projects.Practical implications - The proposed methodology is expected to support requirements elicitation, and facilitate early stages of PSS development.Originality/value - This paper presents empirical findings regarding consumer acceptance, and provides a detailed analysis of factors that are central to PSS acceptance. It also introduces methodology for description and analysis of the complex consequences a solution may have from a consumer perspective.

  • 452.
    Rhen, Ida-Märta
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Gyllensvärd, Dan
    Halmstad University.
    Hanson, Lars
    Chalmers University of Technology.
    Högberg, Dan
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Time dependent exposure analysis and risk assessment of a manikin's wrist movements2011In: Proceedings of DHM, First International Symposium on Digital Human Modeling, Université Claude Bernard Lyon , 2011Conference paper (Refereed)
    Abstract [en]

    The increased computerisation of design and engineering work has led to the development of software such as digital human modeling (DHM) tools. These tools can be used to simulate and visualise human work and for evaluating ergonomic conditions. Observational-based ergonomics methods, such as RULA and OWAS, may be used for characterizing workload and are typically integrated in DHM tools. However, using observational-based methods usually means that ergonomics evaluations are based on assessing static postures, i.e. not taking time-related aspects into account. This is usually also the case when industrial companies choose to customize the DHM software and integrate their own company specific ergonomics methods. Since time-sensitive aspects, such as frequency and angular velocity, are of importance in the ergonomics evaluation, it is necessary to be able to predict these variables also in an early stage of the design process. As observation methods are poor in terms of sensitivity they give rough estimations of ergonomic conditions. Consequently, researchers aim to develop dynamic evaluation methods where also time-dependent aspects, such as repetitiveness, velocity and duration in exposed positions, are considered. The objective of this study is to focus on the assessment of the wrist since a large amount of work related disorders is caused by this joint and also due to the fact that this is a relatively uncomplicated joint to explore from a biomechanical perspective. This paper displays initial findings from the literature for how to adequately and quantitatively assess wrist movements, appropriate evaluation criteria and for how to calculate cumulative load. Moreover, the paper illustrates how time significant wrist exposure data obtained from a DHM may be used for ergonomics assessment. This involves a proposed time consideration concept, based on a combination of modified and established evaluation methods, including suggestions for how to identify fundamental cycles.

  • 453.
    Rhen, Ida-Märta
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Högberg, Dan
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Hanson, Lars
    Chalmers University of Technology / Industrial Development Scania CV.
    Bertilsson, Erik
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Dynamic wrist exposure analysis of a digital human model2012In: Proceedings of the 4th International Conference on Applied Human Factors and Ergonomics, CRC Press, 2012, p. 3944-3953Conference paper (Refereed)
    Abstract [en]

    Simulation and visualisation software such as digital human modelling (DHM) tools have been designed to predict and evaluate ergonomics within a virtual environment. Today’s DHM tools typically include observation-based evaluation methods, initially designed for visual observation. Direct measurement techniques enable assessment of quantitative data similar to the information derived from the DHM. Such technique allows detailed and time-dependent risk aspects to be considered in the ergonomics evaluation. No methods in commercial DHM software calculate time-dependent information, which is shown as an important risk factor in the development of work-related musculoskeletal disorder (WMSD). This paper presents and discusses an ergonomics assessment approach based on the theory of a dose-response relationship between exposure and the risk of arising injury. The focus of the approach presented is on the wrist-joint.

  • 454.
    Rhén, Ida-Märta
    et al.
    Department of Industrial and Materials Science, Chalmers University of Technology.
    Forsman, Mikael
    IMM Institute of Environmental Medicine, Karolinska Institutet.
    Örtengren, Roland
    Department of Industrial and Materials Science, Chalmers University of Technology.
    Högberg, Dan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Keyvani, Ali
    Robotics and Automation, Virtual Manufacturing AB, Göteborg.
    Lämkull, Dan
    Global Strategy and Process Development, Volvo Car Corporation, Manufacturing Engineering, Göteborg.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Industrial Development, Scania CV, Södertälje / Department of Industrial and Materials Science, Chalmers University of Technology.
    Ergonomic risk assessment in DHM tools employing motion data: Exposure calculation and comparison to epidemiological reference data2018In: International Journal of Human Factors Modelling and Simulation, ISSN 1742-5549, Vol. 6, no 1, p. 31-64Article in journal (Refereed)
    Abstract [en]

    Digital human modelling (DHM) allows ergonomic risk assessment to be performed at early stages of design and development. Such assessment is typically based on observational methods, which do not take advantage of the potential of DHM tools to provide precise posture and motion data. This paper describes and illustrates an alternative assessment approach employing DHM tools, inspired by risk assessment based on direct measurements. A literature survey established a reference database of epidemiological associations between exposure and wrist-related disorders. This approach is illustrated by a DHM simulation of a car assembly task. Wrist posture and motion were simulated and compared to the database, predicting the prevalence of work-related musculoskeletal disorders on the basis of direct measurements.

  • 455.
    Rhén, Ida-Märta
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Hanson, L.
    Chalmers University of Technology.
    Högberg, Dan
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Risk exposure assessment of dynamic wrist motions of a digital human model2011In: Proceedings of the 43rd Annual Nordic Ergonomics Society Conference, University of Oulu , 2011, p. 386-391Conference paper (Refereed)
    Abstract [en]

    Software tools as Digital Human Modelling (DHM) has been designed to simulate and visualize the human work and to assess ergonomics conditions. No methods in commercial DHM software calculate time-dependent information, which is adequate in defining the relations between exposure and risk of disorders. The paper presents and discusses how an assembly task can be analysed with the help from a DHM-tool. Out comes from the study revealed that it is possible to generate output files with time-dependent wrist exposure data from a manikin in a DHM-tool. However, current evaluation methods do not take time-dependent information into consideration.

  • 456.
    Ruiz Castro, Pamela
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Högberg, Dan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ramsen, Håkan
    Volvo Trucks, Gothenburg, Sweden.
    Bjursten, Jenny
    Volvo Trucks, Gothenburg, Sweden.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Industrial Development, Scania, Södertälje, Sweden.
    Virtual Simulation of Human-Robot Collaboration Workstations2019In: Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018): Volume V: Human Simulation and Virtual Environments, Work With Computing Systems (WWCS), Process Control / [ed] Sebastiano Bagnara, Riccardo Tartaglia, Sara Albolino, Thomas Alexander, Yushi Fujita, Springer, 2019, Vol. 822, p. 250-261Conference paper (Refereed)
    Abstract [en]

    The constant call in manufacturing for higher quality, efficiency, flexibility and cost effective solutions has been supported by technology developments and revised legislations in the area of collaborative robots. This allows for new types of workstations in industry where robots and humans co-operate in performing tasks. In addition to safety, the design of such collaborative workstations needs to consider the areas of ergonomics and task allocation to ensure appropriate work conditions for the operators, while providing overall system efficiency. The aim of this study is to illustrate the development and use of an integrated robot simulation and digital human modelling (DHM) tool, which is aimed to be a tool for engineers to create and confirm successful collaborative workstations. An assembly scenario from the vehicle industry was selected for its redesign into a collaborative workstation. The existing scenario as well as potential collaborative concepts are simulated and assessed using a version of the simulation tool IPS IMMA. The assembly use case illustrates the capabilities of the tool to represent and evaluate collaborative workstations in terms of ergonomics and efficiency assessments.

  • 457.
    Ruiz Castro, Pamela
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Mahdavian, Nafise
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Brolin, Erik
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Högberg, Dan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Industrial Development, Scania, Södertälje, Sweden.
    IPS IMMA for designing human-robot collaboration workstations2017In: Proceedings of the 5th International Digital Human Modeling Symposium / [ed] Sascha Wischniewski & Thomas Alexander, Federal Institute for Occupational Safety and Health , 2017, p. 263-273Conference paper (Refereed)
    Abstract [en]

    The global competition has forced manufacturing companies to further increase their productivity. This, together with technology development and changes in regulations, have led to the introduction of new types of workstations in production lines, where human operators collaborate with industrial robots to perform work tasks. As any type of product, these workstations need to be designed in the most optimal way to deliver the expected value. In the design process of these collaborative workstations, separate virtual simulations of industrial robots and human operators can be made with multiple commercial software. Separate simulations reduce the efficiency of the design process and makes it harder to identify successful design solutions. Hence, there is a need for software tools that are capable of simultaneous simulation of the human-robot collaboration in a workstation. Providing engineers with such tools will assist their tasks to optimize the human and robot workflow, while proactively ensuring proper ergonomic conditions for operators.This paper describes and illustrates how the digital human modelling (DHM) tool IPS IMMA can aid in the design of human-robot collaboration workstations. A use case where the human operator collaborates with a robot to produce a section of a pedal car in a virtual scenario is described. The use case illustrates the current capabilities and limitations of the software to simulate human-robot collaborations in workstations. Hence, the use case aims to provide input for further development of DHM tools aimed to assist the design of human-robot collaboration workstations.

  • 458.
    Ruiz Zúñiga, Enrique
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    A simulation-based approach for optimization of production logistics with consideration to production layout: Research Proposal2016Report (Other academic)
    Abstract [en]

    Manufacturing sectors in Sweden have a long tradition and represent a significant share of the national gross domestic product and the export values. Most of the Swedish manufacturing companies have gone through a modernization and adaptation process in order to be able to compete on a globalized market. Many plants, however, still have non-optimized shop floors as a consequence of adaptations over time without redesigning its production and logistics flows and with a lack of an overall strategy. To support the optimization of shop floors, this project suggests the combined use of Discrete-Event Simulation (DES) and Simulation-Based Multi-objective Optimization (SBO) under the umbrella of a design and creation research strategy. The aim of the project is to support the improvement and optimization of high product mix and a low-volume of customized products manufacturing systems by considering production and logistics flows along with the shop floor layout. The methodology is intended to contribute to significantly increase the productivity and efficiency of the Swedish manufacturing industry and help companies to survive on the globalized market. The potential results can serve for decision makers and stakeholders to apply changes and adaptations in the system considering the mid and long term goals of the company. Going through different case studies implemented in a middle-size water pumps manufacturer, this methodology will be useful in practice and it will provide a decision support system for this specific industrial partner and will serve as a guideline for other manufacturing companies.

  • 459.
    Ruiz Zúñiga, Enrique
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Improvement of the service level of an Emergency Department using Discrete event simulation2015Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Emergency departments in Sweden are usually struggling with long waiting times, delays and bottlenecks in the system. The National Board of Health and Welfare and the County Council of Västra Götaland have established to decrease the average time a patient stays in an emergency department as important priority as well as the waiting time to be seen by a nurse and by a physician.

    Healthcare systems are usually characterized by its complexity because of the variability and stochastic nature of the different processes involved in the flow of patients, staff and resources. In order to increase the use of the existing resources and to reduce the waiting times of patients, a system improvement methodology involving discrete-event simulation and process analysis has been used. In this project a computer-based simulation tool was applied at the emergency department of the hospital Kärnsjukhuset in Skövde, which belongs to Skaraborgs Sjukhus and is one of the largest emergency departments in the region of Västra Götaland. A three-dimensional model was created to help visualize and understand the problems, as well as to identify improvements by the different stakeholders involved. Continually, the simulation model was modified to test possible improved scenarios with the aim to increase the service level of the system. 

    The design, implementation and analysis of these scenarios have provided decision makers of the emergency department with the necessary information to implement or reject the ideas of the different improved scenarios. Some of these scenarios had a significant impact with small changes so they were implemented in the real system; some others had non-significant impact in the results so they were not implemented. The main result of this project has been to identify which system changes will lead to a reduction of the different waiting times of patients. In addition, the simulation and experiments of future solutions show a more efficient use of the existing resources. This design of a better configuration of the system gives Kärnsjukhuset the possibility to increase the service level of the system and to meet some of the requirements established by the County Council. This project shows that the use of simulation tools provides enormous benefits for healthcare system analysis and improvement; new ideas and scenarios can be designed without disturbing the normal activities of the hospital, saving considerable time, money and resources.

  • 460.
    Ruiz Zúñiga, Enrique
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Flores García, Erik
    Innovation and Product Realisation, Mälardalen University.
    Urenda Moris, Matías
    Division of Industrial Engineering and Management, Uppsala University.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Challenges of Simulation-based Optimization in Facility Layout Design of Production Systems2019In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 507-512Conference paper (Refereed)
    Abstract [en]

    Facility layout design (FLD) is becoming more challenging than ever. In particular, modern day manufacturing industry requires advancing from a traditional approach of mass production to one of mass customization including increased flexibility and adaptability. There are several software tools that can support facility layout design among which simulation and optimization are the most powerful – especially when the two techniques are combined into simulation-based optimization (SBO). The aim of this study is to identify the challenges of SBO in FLD of production systems. In doing so, this paper uncovers the challenges of SBO and FLD, which are so far addressed in separate streams of literature. The results of this study present two novel contributions based on two case studies in the Swedish manufacturing industry. First, that challenges of SBO in FLD, previously identified in literature, do not hold equal importance in industrial environments. Our results suggest that challenges in complexity, data noise, and standardization take precedence over challenges of SBO in FLD previously reported in literature. Second, that the origin of challenges of SBO in FLD are not technological in nature, but stem from the increased complexity of factories required in modern day manufacturing companies.

  • 461.
    Ruiz Zúñiga, Enrique
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    The Internet of Things, Factory of Things and Industry 4.0 in Manufacturing: Current and Future Implementations2017In: Advances in Manufacturing Technology XXXI: Proceedings of the 15th International Conference on Manufacturing Research, Incorporating the 32nd National Conference on Manufacturing Research, September 5–7, 2017, University of Greenwich, UK / [ed] James Gao, Mohammed El Souri, Simeon Keates, IOS Press, 2017, p. 221-226Conference paper (Refereed)
    Abstract [en]

    In the currently rapidly changing industrialized world, globalization,product customization and automation are playing an imposing role in thedevelopment of the manufacturing sector. Nowadays, the innovative concepts ofThe Internet of Things, Factory of Things and Industry 4.0 are aimed torevolutionize the way technology can help improve production around the world.While in some international corporations these concepts are being deeply studiedand are starting to be implemented, also in middle-size and large manufacturers itis clear they could contribute with many advantages; however, skepticism anduncertainty are still present among managers and stakeholders. In this paper, thecurrent and coming state-of-the-art technology and implementation of the Factoryof Things paradigm are presented and examples of the current implementation inglobal manufacturing companies are analyzed. Additionally, this article willdiscuss the potential implementation of this Industry 4.0 in a large manufacturer,and how it can help increase the control and efficiency of production, materialflows, internal logistics and production planning.

  • 462.
    Ruiz Zúñiga, Enrique
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matias
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Integrating Simulation-Based Optimization, Lean, and the Concepts of Industry 4.02017In: Proceedings of the 2017 Winter Simulation Conference / [ed] W. K. V. Chan, A. D'Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, E. Page, IEEE, 2017, p. 3828-3839Conference paper (Refereed)
    Abstract [en]

    Nowadays, due to the need of innovation and adaptation for the mass production of customized goods,many industries are struggling to compete with the manufacturing sector emerging in different countriesaround the world. The understanding and implementation of different improvement techniques isnecessary in order to take part in the so-called fourth industrial revolution, Industry 4.0. This paperinvestigates how two well-known improvement approaches, namely lean and simulation-basedoptimization, can be combined with the concepts of Industry 4.0 to improve efficiency and avoid movingproduction to other countries. Going through an industrial case study, the paper discusses how such acombination could be carried out and how the different strengths of the three approaches can be utilizedtogether. The case study focuses on how the efficiency of a production site can be increased and howIndustry 4.0 can support the improvement of the internal logistics on the shop floor.

  • 463.
    Ruiz Zúñiga, Enrique
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    A simulation-based multi-objective optimization approach for production and logistics considering the production layout2016In: Proceedings of the 7th Swedish Production Symposium, 2016Conference paper (Refereed)
    Abstract [en]

    Manufacturing sectors in Sweden have a long tradition and represent a significant share of the national gross domestic product and the export values. Most of the Swedish manufacturing companies have gone through a modernization and adaptation process in order to be able to compete on a globalized market. Many plants, however, still have non-optimized shop floors as a consequence of the shop floors being adapted over time without redesigning its production and logistics flows and with a lack of an overall strategy. To support the optimization of shop floors, this paper suggests the combined use of Discrete-Event Simulation and Simulation-Based Multi-objective Optimization. The aim of the paper is to analyze a simulation methodology that supports the optimization of shop floors by considering production and logistics flows along with the shop floor layout. The methodology is intended to contribute to significantly increase the productivity and efficiency of the Swedish manufacturing industry and help companies to survive on the globalized market. Through a case study, the paper shows that the proposed methodology is useful in practice and that it provides a decision support system for manufacturing companies.

  • 464.
    Ruiz Zúñiga, Enrique
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Production Logistics Design and Development Support: A Simulation-Based Optimization Case Study (WIP)2016In: SummerSim'16, 2016 July 24-27, Palais des congres de Montreal (Montreal Convention Center) | Montreal, Quebec, Canada / [ed] Society for Modeling & Simulation International (SCS), The Society for Modeling and Simulation International, 2016, p. 56:1-56:6, article id 56Conference paper (Refereed)
    Abstract [en]

    Manufacturing sectors in Sweden have a long history that leads to common non-optimized flows on the shop floor. Especially when having a really high product mix and a low-volume of customized products, a great deal of effort with respect to flow optimization is needed to stay present and compete in the globalized market. The goal of this project is to support the design and development of the implementation of new production systems and logistics flows considering the shop floor plant layout of a Swedish middle-size water pumps factory. In this paper, with the help of different types of simulation models and optimization, some results of a new technologically adapted production line are analyzed and relevant information and potential improvements in the production are found. The further development of optimization studies using the exiting simulation models is stated as ongoing and future work. The obtained and potential results can serve for decision makers and stakeholders to apply changes and adaptations in the system considering the mid and long term goals of the company.

  • 465.
    Ruiz-Amurrio, Maria
    et al.
    MGEP, Dept Mecan Prod & Ind, Arrasate Mondragon, Spain.
    Elorza, Unai
    MGEP, Dept Mecan Prod & Ind, Arrasate Mondragon, Spain.
    Linnéusson, Gary
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Zabaleta-Etxebarria, Noemi
    MGEP, Dept Mecan Prod & Ind, Arrasate Mondragon, Spain.
    Identification of the factors which influence employee commitment using systems thinking = Identificación de factores que influyen en el compromiso de los empleados utilizando pensamiento sistemico2018In: DYNA, ISSN 0012-7361, Vol. 93, no 5, p. 504-511Article in journal (Refereed)
    Abstract [en]

    In our increasingly globalised economy, managing continuous change and remaining competitive has become a central issue for organisations in the industrial sector. Building a sustainable competitive advantage through effective decision making and the use of decision making tools has been widely studied [1,2]. The success of a company will be dependent on the skills of the workers, their capacity for learning, and adapting to special and evolving client necessities. Culture change via, communication and participation are the elements of change identified for engineering companies [3]. Thus, the main objective of this research is to understand the behaviour of commitment, the variables that influence it and the variables that are influenced by it. Commitment is considered a key factor due to its influence on performance. The methodology that was followed was based on the modelling methodology proposed by Sterman [4]. The first step was the problem definition, the second step was data collection. The purpose was to define the feedback loops of which the conceptual model (CM) is composed. Thirdly, conceptual model definition was developed. As a result, the outcome that is achieved through this research is a conceptual model. The main function of this model is to facilitate the understanding of the behaviour of commitment through Systems Thinking tools. This research contributes to both Strategic Human Resource Management (SHRM) and Systems Thinking (ST) fields of study. The most notable contribution for ST is the fact of combining more than one input source (Literature + Group Model Building + prior research) for the conceptual model definition. The combination of these input sources for an ST model is not common in the scientific community. Moreover, the use of ST in SHRM is limited.

  • 466.
    Rösiö, Carin
    et al.
    Jönköping University School of Engineering, Department of Industrial Engineering and Management, Jönköping, Sweden.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Srikanth, Karthik Banavara
    Jönköping University School of Engineering, Department of Industrial Engineering and Management, Jönköping, Sweden.
    Shetty, Savin
    Jönköping University School of Engineering, Department of Industrial Engineering and Management, Jönköping, Sweden.
    Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry2019In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 28, p. 76-82Article in journal (Refereed)
    Abstract [en]

    To increase changeability and reconfigurability of manufacturing systems, while maintaining cost-efficiency and environmental sustainability they need to be designed in accordance to the need for change. Since companies often need to convert existing manufacturing systems to handle variation, implementation of reconfigurable manufacturing systems calls for an analysis of the current system to understand to what extent they fulfil reconfigurability characteristics. This requires an assessment of the needs for reconfigurability as well as assessment of the existing ability to reconfigure the manufacturing system. Although a lot of reconfigurable manufacturing system assessment models are proposed in theory there is an evident knowledge gap pertaining to what extent the existing systems in the industry are in achieving reconfigurability. The purpose with this paper is to propose an assessment criterion for existing manufacturing systems to measure reconfigurability and their readiness to change with respect to products and volume variations. Based on a literature review of existing reconfigurability assessment models and a case study within the automotive industry, a criterion is developed and tested to analyze how reconfigurable a system is and to decide which parameters that need more attention to achieve higher degree of reconfigurability. © 2019 The Authors.

  • 467.
    Sachdeva, Nitin
    et al.
    Department of Operational Research, University of Delhi, Faculty of Mathematical Sciences, Delhi, India.
    Singh, Ompal
    Department of Operational Research, University of Delhi, Faculty of Mathematical Sciences, Delhi, India.
    Kapur, P. K.
    Amity University, Noida, India.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 3, p. 316-324Article in journal (Refereed)
    Abstract [en]

    Today technology that learns from data to forecast future behavior of individuals, organizations, government and country as a whole, is playing a crucial role in the advancement of human race. In fact, the strategic advantage most of the companies today strive for are use of new available technologies like cloud computing and big data. However, today's dynamic business environment poses severe challenges in front of companies as to how to make use of the power of big data with the technical flexibility that cloud computing provides? Therefore, evaluating, ranking and selecting the most appropriate cloud solution to manage big data project is a complex concern which required multi criteria decision environment. In this paper we propose a hybrid TOPSIS method combined with intuitionistic fuzzy set to select appropriate cloud solution to manage big data projects in group decision making environment. In order to collate individual opinions of decision makers for rating the importance of various criteria and alternatives, we employed intuitionistic fuzzy weighted averaging operator. Lastly sensitivity analysis is performed so as to evaluate the impact of criteria weights on final ranking of alternatives.

  • 468.
    Salimi, Saeed
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Application of cohesive modeling in joining technology: Thick adhesive layers and rivet joints2012Licentiate thesis, monograph (Other academic)
    Abstract [en]

    This thesis summarizes the development of cohesive modeling of joints. It presents some new developments regarding the effects of non-zero thickness of adhesive layers and a novel approach of using the concept of cohesive modeling to characterize the failure behavior of rivet joints. The failure behavior of a thick adhesive layer loaded in mode I (peel), mode II (shear) and mixed-mode are studied. Analytical relations are derived for the energy release rate of DCBa-, ENFb- and MCBc-tests for pure peel, shear and mixed modes of loading, respectively. Consequently, cohesive laws are derived from the energy release rate. The results are used to predict the failure of three sets of TRBd-tests with similar and dissimilar adherents bonded with a thick layer of adhesive and loaded in mixed mode. Moreover, a model to characterize the failure behavior of rivet joints is investigated and presented. Data from DCB-, ENF- and MCB-experiments are evaluated and used to simulate and predict the failure behavior of TRB-tests. The results of simulations are verified by the results of three sets of TRB-experiments. To this end, sixteen TRB-experiments are carried out in this work. The main achievement of this thesis is validating the use of cohesive modeling to model adhesively bonded joints with dissimilar adherents bonded with a thick layer of adhesive. The proposed model for studying the failure behavior of rivet joints is also found to show good agreement with numerical analyses. a Double Cantilever Beam b End Notch Flexure c Mixed-mode Cantilever Beam d Tensile Reinforced Bending.

  • 469.
    Salomonsson, Kent E.
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Andersson, Tobias J.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Weighted Potential Methodology for Mixed Mode Cohesive Laws2010In: Proceedings of the MECOM DEL BICENTENARIO, IX Argentinian Congress on Computational Mechanics / [ed] Eduardo Dvorkin, Marcela Goldschmit, Mario Storti, Asociación Argentina de Mecánica Comptacional , 2010, p. 8355-8374Conference paper (Refereed)
    Abstract [en]

    A  weighted  potential  methodology  is  developed  by  utilizing  pure  mode  I  and mode  II  energy  release  rate  experiments  to  determine  the  traction-separation  relations  for thin  adhesive  layers.  The  experimentally  measured  energy  release  rates  act  as  boundary conditions  for  developing  a  weighted  potential  function.  Thus,  the  tractions  for  any  mixed mode loading can be established.  Changes of mode mix during an experiment can also be captured  by  the  law  since  every  mixed  mode  variation  is  given  by  the  potential  function. Furthermore,  by  use  of  an  inverse  J-integral  approach  and  damage  type  variables,  the traction-separation  relations  for  any  mode  mix  can  be  approximated  by  use  of  pure  mode experiments.  Numerical  simulations  show  the  applicability  of  the  methodology.  The  results indicate  that  the  methodology  is  promising  when  simulating  the  constitutive  behavior  of adhesive layers.

  • 470.
    Salomonsson, Kent
    et al.
    Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States.
    Stigh, Ulf
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Influence of root curvature on the fracture energy of adhesive layers2009In: Engineering Fracture Mechanics, ISSN 0013-7944, E-ISSN 1873-7315, Vol. 76, no 13, p. 2025-2038Article in journal (Refereed)
    Abstract [en]

    Previously performed experiments to study the mode I behavior of an adhesive layer revealed an apparent increase in the fracture toughness when the adherends deformed plastically. Attempts to simulate the experiments are made; both with elastically and plastically deforming adherends. Thus, effects of the size of the process zone and the deformation of the adherends are revealed. The adhesive layer is modeled using finite elements with different approaches; cohesive elements and representative volume elements. The adherends are modeled with solid elements. With a long process zone, all models give good results as compared to the experiments. However, only the model with representative volume elements gives good agreement for large root curvatures and correspondingly short process zones. The results are interpreted by analyzing the deformation and mechanisms of crack propagation in the representative volume elements. It is shown that with large root curvature of the adherends, the in-plane stretching of the adhesive layer gives a substantial contribution to the fracture energy. A simple formula is derived and shown to give an accurate prediction of the effects of the root curvature. This result indicates the limits of conventional cohesive zone modeling of an adhesive layer of finite thickness.

  • 471.
    Sandberg, Ulf
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Kungliga Tekniska Högskolan.
    Management of factory and maintenance information for multiple production and product life-cycle phases2014In: / [ed] B.K.N.Rao, 2014Conference paper (Refereed)
    Abstract [en]

    Maintenance is crucial for future manufacturing systems. An extended local knowledge is essential to increase precision and efficiency, but also for improvements of the maintained object itself. Approaches exist that closes the loop from end-user to vendor, but intra loops are not so well developed.

    This article discusses ways to interconnect and manage data and knowledge flow between work processes in user and vendor life-cycles. It aims to inspire improvements in existing approaches, closer connections between producer and customer, between users, and improved quality of maintenance work via factory-, company-, or group-wide data and knowledge about similar types of equipment.

  • 472.
    Saranen, Juha
    et al.
    Lappeenranta University of Technology.
    Hilmola, Olli-Pekka
    Lappeenranta University of Technology.
    Ujvari, Sandor
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Modelling Stochastic Elements in Transportation System Simulation: Evidence from Four Projects2008In: Fourth International Railway Logistics Seminar: Co-operation among Transportation Modes in Northern Europe, Lappeenranta University of Technology , 2008, p. 53-78Conference paper (Refereed)
    Abstract [en]

     

    Discrete event system simulation is often seen as a genuine tool to investigate the performance of transportation systems. The complexity of real-world systems often prevents us from accurately describing these by a mathematical model that can be evaluated analytically, thus, simulation is often the only realistic alternative. Another advantage of the simulation is the ability to include statistical analysis for different simulation scenarios.

    In this paper we discuss the main problems concerning the modelling of transportation systems. Well-known approaches of incorporating uncertainty into models include trace driven simulations and sampling directly from gathered data (this latter could also be presented by a fitting statistical distribution). Another aspect to be taken into account is the economics of simulation modelling; a more detailed model requires additional building time, and proper treatment of stochastic models requires statistical analysis, which again usually implies several simulation runs. From this outset the following question arises: Should stochastic behaviour be included in transportation simulation models in the first place at all?

    We present real case examples including evaluation of a railway transportation concept, capacity analysis of an automatic guided vehicle system, CBA of a railway network investment and evaluation of different multipurpose railway wagons, where stochastic behaviour is dealt with in different ways. Based on the cases we make an initial attempt to formulate framework for deciding how to include stochastic behaviour in the simulation model. We stress that the metrics used to evaluate system performance should be included in the framework. For further research topics we suggest formulating explicit guidelines to deal with stochastics to increase the efficiency of model building.

     

  • 473.
    Schmidt, Bernard
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Information Fusion processes in Prognostics and Health Management2014Conference paper (Other academic)
    Abstract [en]

    Information Fusion plays important role in Prognostics and Health Management, where data and informations from different sources need to be combined, analyzed and finally used or presented for proper maintenance decisions. The objective of this paper is to outline and analyzed the relation between Information Fusion process and data/information processing in Prognostics Condition Based Maintenance. The Data Fusion Information Group Model (DFIGM) is presented as well as distinction between two levels of information fusion: (1) low-level information fusion (LLIF), which addresses the signal processing, object state estimation and characterization, and (2) high-level information fusion (HLIF) focused on control and situational understanding. All this processes are aligned with condition based maintenance processes from data acquisition and processing, through diagnostics, prognostics, up to health management. Presented work is one of the first steps in the research project toward improvements in Condition Based Maintenance with focus on its implementation in the manufacturing industry.

  • 474.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Toward Predictive Maintenance in a Cloud Manufacturing Environment: A population-wide approach2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The research presented in this thesis is focused on improving industrial maintenance by using better decision support that is based on a wider range of input information. The core objective is to research how to integrate information from a population of similar monitored objects. The data to be aggregated comes from multiple disparate sources including double ball-bar circularity tests, the maintenance management system, and the machine tool’s controller. Various data processing and machine learning methods are applied and evaluated. Finally, an economic evaluation of the proposed approach is presented. The work performed is presented in five appended papers.

    Paper I presents an investigation of cloud-based predictive maintenance concepts and their potential benefits and challenges.

    Paper II presents the results of an investigation of available and potentially useful data from the perspective of predictive analytics with a focus on the linear axes of machine tools.

    Paper III proposes a semantic framework for predictive maintenance, and investigates means of acquiring relevant information from different sources (i.e., ontology-based data retrieval).

    Paper IV presents a method for data integration. The method is applied to data obtained from a real manufacturing setup. Simulation-based evaluation is used to compare results with a traditional time-based approach.

    Paper V presents the results from additional simulation-based experiments based on the method from Paper IV. The aim is to improve the method and provide additional information that can support maintenance decision-making (e.g., determining the optimal interval for inspections).

    The method developed in this thesis is applied to a population of linear axes from a set of similar multipurpose machine tools. The linear axes of machine tools are very important, as their performance directly affects machining quality. Measurements from circularity tests performed using a double ball-bar measuring device are combined with event and context information to build statistical failure and classification models. Based on those models, a decision-making process is proposed and evaluated. In the analysed case, the proposed approach leads to direct maintenance cost reduction of around 40 % compared to a time-based approach.

  • 475.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Diego, Galar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Sweden.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH.
    Asset management evolution: from taxonomies toward ontologies2015In: Maintenance, Condition Monitoring and Diagnostics, Maintenance Performance Measurement and Management / [ed] Sulo Lahdelma and Kari Palokangas, Oulu, Finland: POHTO , 2015Conference paper (Refereed)
    Abstract [en]

    This paper addresses the evolution that can be observed in Asset Management in modelling approach. Most traditional Condition Monitoring systems use hierarchical representations of monitored the integration of data from disparate source toward context awareness and Big Data utilization there is a need to include and model more complicated dependencies than hierarchical. Ontology based modelling is gaining recently on popularity in the domain of Condition Monitoring and Asset Management.

  • 476.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Big data in maintenance decision support systems: aggregation of disparate data types2016In: Euromaintenance 2016 ConferenceProceedings, 2016, p. 503-512Conference paper (Other academic)
    Abstract [en]

    There is need to obtain reliable information on current and future asset health status to support maintenance decision making process. Within maintenance two main sources of data can be distinguished: Computerized Maintenance Management System (CMMS) for asset registry and maintenance work records; and Condition Monitoring Systems (CM) for direct asset components health state monitoring. There are also other sources of information like SCADA (Supervisory Control and Data Acquisition) for process and control monitoring that can provide additional contextual information leading to better decision making. However data produced acquired and processed and in those system are of disparate types, nature and granularity. This variety includes: event data about failures or performed maintenance work mostly descriptions in unstructured natural language; process variables obtained from different types of sensors and different physical variables from transducers, acquired with different sampling frequencies. Indeed, condition monitoring data are so disparate in nature that maintainers deal with scalars (temperature) through waveforms (vibration) to 2D thermography images and 3D data from machine geometry measuring. Integration and aggregation of those data is not a trivial task and requires modelling of knowledge about those data types, their mutual dependencies and dependencies with monitored processes. There are some attempts of standardisation that try to enable integration of CBM data from different sources. The conversion of those amount of data in meaningful data sets is required for better machine health assessment and tracking within the specific operational context for the asset. This will also enhance the maintenance decision support system with information on how different operational condition can affect the reliability of the asset for concrete contextual circumstances.

  • 477.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Context Awareness in Predictive Maintenance2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, p. 197-211Chapter in book (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

  • 478.
    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.
    Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1327-1332Article in journal (Refereed)
    Abstract [en]

    The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.

  • 479.
    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
    School of Engineering Science, Kungliga Tekniska Högskolan, Stockholm, Sweden.
    Galar, Diego
    Department of Civil, Environmental and Natural Resources Engineering, Luleå Tekniska Universitet, Luleå, Sweden.
    Context preparation for predictive analytics – a case from manufacturing industry2017In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed)
    Abstract [en]

    Purpose

    The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.

    Design/methodology/approach

    This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.

    Findings

    Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

    Originality/value

    This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

  • 480.
    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.

  • 481.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Mohammed, Abdullah
    Royal Institute of Technology 100 44 Stockholm, Sweden.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering Royal Institute of Technology 100 44 Stockholm, Sweden.
    Minimising Energy Consumption for Robot Arm Movement2013In: Proceedings of the International Conference on Advanced Manufacturing Engineering and Technologies / [ed] Andreas Archenti, Antonio Maffei, Stockholm, Sweden: KTH Royal Institute of Technology, 2013, p. 125-134Conference paper (Refereed)
    Abstract [en]

    Optimising the energy consumption of robot movements has been one of the main focuses for most of today’s robotic simulation software. This optimisation is based on minimising a robot’s joints movements. In many cases, it does not take into consideration the dynamic features. Therefore, reducing energy consumption is still a challenging task and it involves studying the robot’s kinematic and dynamic models together with application requirements. The primary focus of this research is to develop an optimisation model to reduce the energy consumption in robotic applications. An energy optimisation module reported in this paper was developed using Matlab. By solving the kinematics and dynamics equations of the robot, the module is able to optimise towards the minimum energy consumption of the robot’s movements. Moreover, placement of the targets in robot’s working area that minimise the energy consumption can be suggested. The results show the value of the reported approach as a tool for energy efficient robot path planning.

  • 482.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Sandberg, Ulf
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Department of Production Engineering Royal Institute of Technology, Sweden.
    Next Generation Condition Based Predictive Maintenance2014In: Proceedings of The 6th International Swedish Production Symposium 2014 / [ed] Johan Stahre, Björn Johansson, Mats Björkman, 2014Conference paper (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and make decisions upon this prediction. The main aim of the presented research is to achieve an improvement in condition based Predictive Maintenance through the Cloud-based approach with usage of the largest information content possible. The objective of this paper is to outline the first steps of a framework to handle and process maintenance, production and factory related data from the first life-cycle phase to the operation and maintenance phase.

  • 483.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Active collision avoidance for human-robot collaborative manufacturing2012In: Proceedings of the SPS12 conference 2012, The Swedish Production Academy , 2012, p. 81-86Conference paper (Refereed)
    Abstract [en]

    In the human-robot collaborative manufacturing environment where humans and robots coexist, safety protection of human operators in real time is of paramount importance. This paper presents an approach for real-time active collision avoidance in augmented environment, where virtual 3D models of robots and real camera images of operators are used for monitoring and collision detection. A cost-effective depth camera is chosen for surveillance of any mobile foreign objects, including operators, which are not presented in the virtual 3D models. Two redundant Kinect sensors using structured light are used as the depth cameras for better area coverage and for eliminating possible blind spots in the surveillance area. Collision detection is performed by minumum distance. Processing applied on depth images includes background removal, filtering, labeling and points cloud generation. A prototype system is developed and linked to robot controllers for real-time robot control, with zero robot programming. According to the result of collision detection, it can alert an operator, stop a robot, or even move a robot away from an approaching operator. The results of a case study show that this approach can be applied to real-world applications such as human-robot collaborative assembly to safeguard human operators.

  • 484.
    Schmidt, Bernard
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Automatic Robot Calibration via a Global-Local Camera System2012In: Proceedings of FAIM 2012, Tampere University of Technology, 2012Conference paper (Refereed)
    Abstract [en]

    In a human-robot collaborative manufacturing application where working object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic calibration in flexible robotic systems. It consists of two subsystems: a global positioning system based on fixed cameras mounted around robotic workspace, and a local positioning system based on the camera mounted on the robot arm. The aim of the global positioning is to detect work object in working area and roughly estimate the position, whereas the local positioning is to define the object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers have been utilized. For each object, several markers have been used to increase the robustness and accuracy of localization and calibration procedure. This approach can be used in robotic welding or assembly applications.

  • 485.
    Schmidt, Bernard
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. Department of Production Engineering, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Automatic work objects calibration via a global-local camera system2014In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 30, no 6, p. 678-683Article in journal (Refereed)
    Abstract [en]

    In a human–robot collaborative manufacturing application where a work object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic work-object calibration in flexible robotic systems. The approach consists of two modules: a global positioning module based on fixed cameras mounted around robotic workspace, and a local positioning module based on the camera mounted on the robot arm. The aim of the global positioning is to detect the work object in the working area and roughly estimate its position, whereas the local positioning is to define an object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers are utilized. For each object, several markers are used to increase the robustness and accuracy of the localization and calibration procedure. This approach can be used in robotic welding or assembly applications.

  • 486.
    Schmidt, Bernard
    et al.
    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. Department of Production Engineering, KTH.
    Cloud-based Predictive Maintenance2015In: Proceedings of the 25th International Conference on Flexible Automation and Intelligent Manufacturing: Volume I - Designing for Advanced, High Value Manufacturing and Intelligent Systems for the 21st Century / [ed] Chike F. Oduoza, Wolverhampton, UK: The Choir Press , 2015, Vol. 1, p. 224-231Conference paper (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the presented research is to achieve an improvement in Predictive Condition-based Maintenance Decision Making through the Cloud-based approach with usage of wide information content. For the improvement it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production and factory related data from the first lifecycle phase to the operation and maintenance phase.

  • 487.
    Schmidt, Bernard
    et al.
    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. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Cloud-enhanced predictive maintenance2018In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 99, no 1-4, p. 5-13Article in journal (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

  • 488.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Contact-less and programming-less human-robot collaboration2013In: Forty Sixth CIRP Conference on Manufacturing Systems 2013 / [ed] Pedro F. Cunha, Elsevier, 2013, Vol. 7, p. 545-550Conference paper (Refereed)
    Abstract [en]

    In today's manufacturing environment, safe human-robot collaboration is of paramount importance, to improve efficiency and flexibility. Targeting the safety issue, this paper presents an approach for human-robot collaboration in a shared workplace in close proximity, where real data driven 3D model of a robot and multiple depth images of the workplace are used for monitoring and decision-making to perform a task. The strategy for robot control depends on the current task and the information about the operator's presence and position. A case study of assembly is carried out in a robotic assembly cell with human collaboration. The results show that this approach can be applied in real-world applications such as human-robot collaborative assembly with human operators safeguarded at all time.

  • 489.
    Schmidt, Bernard
    et al.
    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. Department of Production Engineering, Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Depth camera based collision avoidance via active robot control2014In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 33, no 4, p. 711-718Article in journal (Refereed)
    Abstract [en]

    A new type of depth cameras can improve the effectiveness of safety monitoring in human–robot collaborative environment. Especially on today's manufacturing shop floors, safe human–robot collaboration is of paramount importance for enhanced work efficiency, flexibility, and overall productivity. Within this context, this paper presents a depth camera based approach for cost-effective real-time safety monitoring of a human–robot collaborative assembly cell. The approach is further demonstrated in adaptive robot control. Stationary and known objects are first removed from the scene for efficient detection of obstacles in a monitored area. The collision detection is processed between a virtual model driven by real sensors, and 3D point cloud data of obstacles to allow different safety scenarios. The results show that this approach can be applied to real-time work cell monitoring.

  • 490.
    Schmidt, Bernard
    et al.
    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.
    Predictive Maintenance: Literature Review and Future Trends2015In: Proceedings of the 25th International Conference on Flexible Automation and Intelligent Manufacturing: Volume I - Designing for Advanced, High Value Manufacturing and Intelligent Systems for the 21st Century / [ed] Chike F. Oduoza, Wolverhampton, UK: The Choir Press , 2015, Vol. 1, p. 232-239Conference paper (Refereed)
    Abstract [en]

    In manufacturing industry machines and systems become more advanced and complicated. Proper maintenance is crucial to ensure productivity, product quality, on-time delivery, and safe working environment. Recently, the importance of the predictive maintenance has been growing rapidly. Well applied predictive maintenance can be in many cases more cost effective than traditional corrective and preventive approaches to maintenance. Targeting this vibrant field, this paper reviews the literature of Predictive Maintenance (PdM). Published literature is systematically categorised and then methodically reviewed and analysed. Methodology for data acquisition, feature extraction, failure detection and prediction are presented. The connection between Maintenance field and Information Fusion has been highlighted. Statistical analysis based on Elsevier’s Scopus abstract and citation database has been performed. Various emerging trends in the field of Predictive Maintenance are identified to help specifying gaps in the literature and direct research efforts.

  • 491.
    Schmidt, Bernard
    et al.
    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.
    Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 17, p. 118-125Article in journal (Refereed)
    Abstract [en]

    In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

  • 492.
    Schmidt, Bernard
    et al.
    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.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Semantic Framework for Predictive Maintenance in a Cloud Environment2017In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16 / [ed] Roberto Teti, Doriana M D'Addona, Elsevier, 2017, Vol. 62, p. 583-588Conference paper (Refereed)
    Abstract [en]

    Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

  • 493.
    Senington, Richard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Software containers as a generic foundation for iec 61499 distributed control systems2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 273-278Conference paper (Refereed)
    Abstract [en]

    It is predicated that in the future factories will be more dependent upon complex distributed control software, where execution and communication occur at the edge of the system, close to the machines that are involved in the manufacturing process. This introduces the problem of how to effectively design, deploy and manage such systems. The IEC 61499 function block standard has been given as one way to solve this problem, proposing applications made from a number of modular event driven blocks that encapsulate algorithms potentially in any programming language. At the same time the Internet of Things and Cloud computing fields have encountered the similar problem of encapsulating, managing and distributing scalable applications, and have been investigating software containers as a solution. This paper proposes to combine these two approaches using containers to enable easy distribution and management of software modules on manufacturing devices while taking advantage of the IEC 61499 application model to enable an application orchestration tool. It describes the properties of container technology that can support the creation of function block like components and the proposal is supported by a prototype version of such a system which has already been implemented.

  • 494.
    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. 

  • 495.
    Senington, Richard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pataki, Balazs
    Hungarian Academy of Sciences Institute for Computer Science and Control, Budapest, Hungary.
    Wang, Xi Vincent
    KTH Royal Institute of Technology.
    Using docker for factory system software management: Experience report2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 659-664Article in journal (Refereed)
    Abstract [en]

    As factories become increasingly computerised, and with the increasing interest in Cyber-Physical-Systems and the Internet-of-Things, the issues of software management, deployment, configuration and integration are expected to become increasingly important. This paper reports on the ongoing experiences of using the Docker container technology in a major EU research project targeting smart factories. Docker is used to distribute, deploy and manage the configuration of multiple software modules between multiple teams and demonstrator sites in multiple locations, where each module can use its own mixture of protocols, programming languages and platforms.

  • 496.
    Sharon, A.
    et al.
    Boston University.
    Ahmad, M. M.University of Teeside.Hägele, M.Fraunhofer-Institut für Produktionstechnik.Villa, A.Turin Polytechnic.Wang, LihuiUniversity of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    2009Collection (editor) (Other academic)
  • 497.
    Siegmund, Florian
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization of Stochastic Systems: Improving the efficiency of time-constrained optimization2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In preference-based Evolutionary Multi-objective Optimization (EMO), the decision maker is looking for a diverse, but locally focused non-dominated front in a preferred area of the objective space, as close as possible to the true Pareto-front. Since solutions found outside the area of interest are considered less important or even irrelevant, the optimization can focus its efforts on the preferred area and find the solutions that the decision maker is looking for more quickly, i.e., with fewer simulation runs. This is particularly important if the available time for optimization is limited, as is the case in many real-world applications. Although previous studies in using this kind of guided-search with preference information, for example, withthe R-NSGA-II algorithm, have shown positive results, only very few of them considered the stochastic outputs of simulated systems.

    In the literature, this phenomenon of stochastic evaluation functions is sometimes called noisy optimization. If an EMO algorithm is run without any countermeasure to noisy evaluation functions, the performance will deteriorate, compared to the case if the true mean objective values are known. While, in general, static resampling of solutions to reduce the uncertainty of all evaluated design solutions can allow EMO algorithms to avoid this problem, it will significantly increase the required simulation time/budget, as many samples will be wasted on candidate solutions which are inferior. In comparison, a Dynamic Resampling (DR) strategy can allow the exploration and exploitation trade-off to be optimized, since the required accuracy about objective values varies between solutions. In a dense, converged population, itis important to know the accurate objective values, whereas noisy objective values are less harmful when an algorithm is exploring the objective space, especially early in the optimization process. Therefore, a well-designed Dynamic Resampling strategy which resamples the solution carefully, according to the resampling need, can help an EMO algorithm achieve better results than a static resampling allocation.

    While there are abundant studies in Simulation-based Optimization that considered Dynamic Resampling, the survey done in this study has found that there is no related work that considered how combinations of Dynamic Resampling and preference-based guided search can further enhance the performance of EMO algorithms, especially if the problems under study involve computationally expensive evaluations, like production systems simulation. The aim of this thesis is therefore to study, design and then to compare new combinations of preference-based EMO algorithms with various DR strategies, in order to improve the solution quality found by simulation-based multi-objective optimization with stochastic outputs, under a limited function evaluation or simulation budget. Specifically, based on the advantages and flexibility offered by interactive, reference point-based approaches, studies of the performance enhancements of R-NSGA-II when augmented with various DR strategies, with increasing degrees of statistical sophistication, as well as several adaptive features in terms of optimization parameters, have been made. The research results have clearly shown that optimization results can be improved, if a hybrid DR strategy is used and adaptive algorithm parameters are chosen according to the noise level and problem complexity. In the case of a limited simulation budget, the results allow the conclusions that both decision maker preferences and DR should be used at the same time to achieve the best results in simulation-based multi-objective optimization.

  • 498.
    Siegmund, Florian
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Virtual Systems Research Centre.
    Sequential Sampling in Noisy Multi-Objective Evolutionary Optimization2009Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Most real-world optimization problems behave stochastically. Evolutionary optimization algorithms have to cope with the uncertainty in order to not loose a substantial part of their performance. There are different types of uncertainty and this thesis studies the type that is commonly known as noise and the use of resampling techniques as countermeasure in multi-objective evolutionary optimization. Several different types of resampling techniques have been proposed in the literature. The available techniques vary in adaptiveness, type of information they base their budget decisions on and in complexity. The results of this thesis show that their performance is not necessarily increasing as soon as they are more complex and that their performance is dependent on optimization problem and environment parameters. As the sampling budget or the noise level increases the optimal resampling technique varies. One result of this thesis is that at low computing budgets or low noise strength simple techniques perform better than complex techniques but as soon as more budget is available or as soon as the algorithm faces more noise complex techniques can show their strengths. This thesis evaluates the resampling techniques on standard benchmark functions. Based on these experiences insights have been gained for the use of resampling techniques in evolutionary simulation optimization of real-world problems.

  • 499.
    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.

  • 500.
    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.

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