Högskolan i Skövde

The aim of this project is integrate the new robot IRB140 from ABB inside the application Wise ShopFloor (Web-based integrated sensor-driven e-ShopFloor) and the integration of a web camera inside the application as well. In order to integrate the ABB IRB140 inside the application, a Java 3D model has to be created, the kinematics and collision constrains have to be defined also and the GUI application modified to fit the virtual model and the camera inside the application. The user has to be able to jog the web camera and zoom it. Changes in the server side have been done in order to introduce new functionalities such as the sessions management, the communication mechanism now is more general using Java inheritance.

Enligt högskoleförordningens examensmål ska studenter efter avslutad utbildning inte bara uppvisa kunskap och förståelse för sitt områdes vetenskapliga grund utan även en rad färdigheter, förmågor, värderingsförmåga och förhållningssätt som inbegriper ett internationellt och interkulturellt perspektiv. Dessa perspektiv kan fås genom internationellt studentutbyte och mobilitet men också genom internationalisering på hemmaplan är tillgängligt för alla studenter. Internationaliseringen bryter språk- och kulturella barriärer, öppnar studenternas internationella perspektiv och bidrar därmed till att öka kvaliteten i utbildningarna och gör studenterna bättre förberedda för ett alltmer mångkulturellt samhälle och en global arbetsmarknad. Personas är fiktiva beskrivningar av typiska användare, förankrade i empiriska data med fokus på användarnas mål, behov, erfarenheter och beteenden. Inom området User Experience Design utgör personas ett viktigt stöd i den användarcentrerade designprocessen. Personas används ofta för att ge liv åt potentiella användare och därmed göra det lättare att ta användarens perspektiv, men har också använts för arbete med utveckling av studenters empatiska förmågor [1] [2]. I detta projekt användes personas för att beskriva studenters mål, behov, erfarenheter och beteenden, i relation till de internationella, interkulturella och globala färdigheter våra studenter behöver i sitt framtida yrkesliv. Syftet är att synliggöra de behov studenterna ska försöka möta och i sin tur söka avspegla detta i utbildningarna. I projektgruppen ingick programansvariga samt personer med erfarenhet av User Experience Design. Efter en kort presentation av projektet och dess resultat genomförs ett digitalt rundabordssamtal.

Syftet med rundabordssamtalet är dels att diskutera allmängiltigheten i de projektresultat som presenterats, dels om personas kan användas för andra ändamål som exempelvis vid utveckling av högre utbildning. Projektledaren fungerar som moderator för samtalet och inleder med en kort Mentimeter-övning där deltagarna får reflektera kring det genomförda projektet och dess relevans för internationalisering i utbildningen. Beroende på antalet deltagare så kan rundabordssamtalet sedan delas in i mindre digitala grupper ledda av övriga projektdeltagare och med en gemensam återsamling med gemensam sammanfattning och avslut. Förväntat utfall från rundabordssamtalet är att sprida arbetssättet med personas men också att vi ska få med oss viktiga synpunkter för det fortsatta arbetet.

[1] Haag, M., & Marsden, N. (2019). Exploring Personas as a Method to Foster Empathy in Student IT Design Teams. International Journal of Technology and Design Education, 29(3), 565–582.

[2] van Rooij, S. W. (2012). Research-Based Personas: Teaching Empathy in Professional Education. Journal of Effective Teaching, 12(3), 77–86.

Despite the increased use of Simulation based Optimization, the design of facility layout is challenged by high levels of uncertainty associatedwith new production processes. Addressing this issue, this paper aims to understand the conceptual modeling activities of Simulation-basedOptimization for facility layout design in conditions of high uncertainty. Based on three in-depth case studies, the results of this paper showhow characterization criteria of production systems can be used in conceptual modelling to reduce uncertainty. These results may be essentialto support managers and stakeholders during the introduction of new production processes in the design of facility layouts.

This study applies an intersectional approach to address concerns about diversity of data acquisition when applying computer vision systems in human order picking. The study draws empirical data from a single case study conducted at an automotive manufacturer. It identifies critical factors of intersectionality for the use of vision systems to enrich data collection in human order picking at four levels including form and function, experience and services, systems and infrastructure, and paradigm and purpose. These findings are helpful for mitigating bias and ensuring accurate representation of the target population in training datasets. The results of our study are indispensable for enhancing human-centricity when applying computer vision systems, and facilitating the acquisition of unstructured data in human order picking. The study contributes to enhancing diversity in human order picking, a situation that is highly relevant because of the variations in age, gender, cultural background, and language of staff. The study discusses theoretical and managerial implications of findings, alongside suggestions for future research.

Companies are continuously working towards system and process improvement to remain competitive in aglobal market. There are different methods that support companies in the achievement of that goal. This paper presents an innovative process that combines lean, simulation and optimization to improve the material flow of a manufacturing company. A description of each step of the process details the lean tools and principles taken into account, as well as the results achieved by the application of simulation and optimization.The project resulted in an improved layout and material flow that employs an automated guided vehicle. In addition, lean wastes related to transport, inventory levels as well as waiting times were reduced. The utilization of the process that combines lean, simulation and optimization was considered valuable for the success of the project.

The improvement of emergency department processes involves the need to take into considerationmultiple variables and objectives in a highly dynamic and unpredictable environment, which makes thedecision-making task extremely challenging. The use of different methodologies and tools to support thedecision-making process is therefore a key issue. This article presents a novel approach in healthcarein which Discrete Event Simulation, Simulation-Based Multi-Objective Optimization and Data Miningtechniques are used in combination. This methodology has been applied for a system improvementanalysis in a Swedish emergency department. As a result of the project, the decision makers were providedwith a range of nearly optimal solutions and design rules which reduce considerably the length of stayand waiting times for emergency department patients. These solutions include the optimal number ofresources and the required level of improvement in key processes. The article presents and discussesthe benefits achieved by applying this methodology, which has proven to be remarkably valuable fordecision-making support, with regard to complex healthcare system design and improvement.

Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.

Healthcare facilities, and especially emergency departments (ED), are usually characterized by its complexity due to the variability and stochastic nature of the processes involved in the system. The combination of different flows of patients, staff and resources also increments the complexity of this kind of facilities. In order to increase its efficiency, many researchers have proposed discrete-event simulation (DES) as a powerful improvement tool. However, DES can be a limited approach in the case a simulation model has too many combinations of input parameters, complex correlations between the input and output parameters and different objective functions. Hence, to find the best configuration of a complex system, an approach combining DES and meta-heuristic optimization becomes an even more powerful improvement technique. Simulation-based multiobjective-optimization (SMO) is a promising approach to generate multiple trade-off solutions particularly when multiple conflicting objectives exist within a complex system. The generated solutions provide decision makers with feasible and optimal alternatives to improve, modify or design healthcare systems. The aim of this paper is to present the work done at the ED of the regional Hospital of Skövde in Sweden, where SMO implemented in modeFromtier has been successfully applied. The result and methodology present a successful approach for decision makers in healthcare systems to reduce the waiting time of patients saving considerable time, money and resources.

Assembly line designs in manufacturing commonly face the key problem of dividing the assembly tasks among the working stations so that the efficiency of the line is optimized. This problem is known as the assembly line balancing problem which is known to be NP-hard. This study, proposes a bi-objective genetic algorithm to cope with the assembly line balancing problem where the considered objectives are the utilization of the assembly line and the workload smoothness measured as the line efficiency and the variation of workload, respectively. The performance of the proposed genetic algorithm is tested through solving a set of standard problems existing in the literature. The computational results show that the genetic algorithm is promising in providing good solutions to the assembly line balancing problem.

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.

Adaptability and flexibility are becoming key concepts in manufacturing. Today manufacturing companies often have to deal with random disruptive events, which necessitates significantly more complex manufacturing systems. Mass customization (manufacturing customized products with mass production efficiency) has also considerably increased the complexity of facility layouts, that is, the physical arrangement of the different aspects required to create products in a factory. Design and improvement of facility layouts is considered a major industrial problem as it affects so many aspects of business. Even in industrialized countries with a long manufacturing history, it is common to find facility layouts that lack optimized flows of materials and products. The main reason for this state of affairs is usually a lack of long-term planning, commonly due to continuous changes and adaptations of the production systems in the layout. These problems are exacerbated by today’s shortened product life cycle.Simulation and optimization are well suited to improve complex manufacturing systems in which several events occur at the same time with unpredictable situations. Thus this thesis aims to investigate how simulation and optimization, and their combination – called simulation-based optimization – can support the redesign and improvement process for existing facility layouts. A literature review shows there is a gap in the field relating to a holistic approach to optimizing facility layouts taking into account production processes and internal logistics. “Holistic” as used here refers to the consideration of the processes and flows occurring in the facility layout, namely machining, assembly, and internal logistics. The aim of this thesis thus includes proposing a holistic methodology based on discrete-event simulation to optimize processes, flows, and internal logistics related to the facility layout.A methodology is defined as a logical set of methods, and in this thesis the methodology has been developed using a case study method with a design and creation strategy. This approach has been successful in identifying and overcoming both theoretical and empirical challenges in simulation-based optimization of facility layout design. The methodology was evaluated using functional resonance analysis method and industrial case studies, and it has proven to be effective for optimizing facility layouts. These results can thus serve as a guideline for engineers and staff involved in this type of layout project, and as a guideline for managers and stakeholders to support strategic decisions.

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.

Globalization and mass customization are commonly translated into increased levels of complexity in manufacturing systems. One of the main reasons is the increased number of variables, parameters, and interrelations on the shop floor. This intrinsic complexity can grow exponentially when considering the manufacture of large-size products with high levels of variability and variants: the mass production of large recreational motorboats with high levels of customization and low production volumes, mass customization. With the increasing role of sustainability and concepts of Industry 5.0, focusing not just on improving production systems but also human wellbeing, quick decision making becomes essential. Data and digitalization are becoming the cornerstone for system improvement, and digital data availability and analysis can facilitate the utilization of computerized tools to support decision making and maximize the performance of complex systems.

For that purpose, simulation can be a powerful analytical tool to design, maintain, and improve complex manufacturing systems. Simulation techniques usually allow handling the size and complexity commonly associated with manufacturing systems. However, in systems with highly customized and large-size products, manual processes, and limited floor space, the implementation of simulation techniques is not straightforward, especially considering the aspects of variability, data collection, model validation, and system reconfiguration. With a particular focus on large-size products and limitations of a constrained existing facility layout, this paper presents the implementation of a simulation-based reconfiguration assessment considering manual production, assembly, and internal logistics requirements.

Going through an industrial case study of large recreational motorboats manufacturing, the paper analyses the system analysis, data collection, implementation, and validation of the methodology step by step. Considering different what-if scenarios, the focus is on the capacity reconfiguration using Discrete-Event Simulation. The results can serve as a guideline for decision-makers and stakeholders working with complex mass customization manufacturing systems and space-constrained facility layouts.

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.

Facility layout design is becoming more challenging as manufacturing moves from traditionally emphasised mass production to mass customisation. The increasing demand for customised products and services is driving the need to increase flexibility and adaptability of both production processes and their material handling systems. A holistic approach for designing facility layouts with optimised flows considering production and logistics systems constraints seems to be missing in the literature. Several tools, including traditional methods, analytic hierarchy process, multiple-attribute decision making, simulation, and optimisation methods, can support such a process. Among these, simulation-based optimisation is the most promising. This paper aims to develop a facility layout design methodology supported by simulation-based optimisation while considering both production and logistics constraints. A literature review of facility layout design with simulation and optimisation and the theoretical and empirical challenges are presented. The integration of simulation-based optimisation in the proposed methodology serves to overcome the identified challenges, providing managers and stakeholders with a decision support system that handles the complex task of facility layout design.

Manufacturing companies worldwide have recently experienced challenging times due to a lack of staff, materials, and components. This has mainly been caused by abrupted logistics chains and collateral effects of the last pandemic situation. Ideally, resilience engineering systems, systems that have recovery capacity from difficulties, are prepared to overcome changes in demand and disruption in production. However, lack of flexibility, adaptability, and available digital data limit the implementation of resilience systems. To overcome this problem with a high number of interrelations considering human-machine interactions, a methodology including Discrete -Event Simulation, Work Domain Analysis, and Functional Resonance Analysis Method is proposed to design, analyze, and improve complex manufacturing systems. These tools allow deeper analysis of the interrelations of the system at different abstraction levels and both with quantitative and qualitative perspectives. Going through an industrial case study, the aim is to increase the capacity and resilience of a leisure-boat manufacturing company producing highly -customized large size products, which adds additional constraints to the problem. The objectives are to increase flexibility and productivity at the same time as maintaining high-quality product standards. The results highlight the identification of some constraints of the system such as the main production bottleneck, lack of space, and a limited number of transports, molds, and skilled personnel. The implementation and results of the methodology have proved to serve as a decision-support tool, providing insight about limitations of the system to managers and stakeholders, as well as a guideline for increasing capacity and resilience of the manufacturing process. Copyright (C) 2022 The Authors.

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.

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.

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.

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.

Optimizing production systems is urgent and indispensable if companies are to cope with global competition and a move from mass production to mass customization. The urgency of this need is more obvious in old production plants with a history of modifications, expansions, and adaptations in their production facilities. It is common to find complex, intricate and inefficient systems of material and product flows as a result of poor production facility layout. Several approaches can be used to support the design of optimal facility layouts. However, there is a lack of a suitable generic methodology for designing such layouts. Additionally, there has been little focus on the data and resources required, or on how simulation and optimization can support the design of optimal facilities. To overcome these deficiencies, this paper studies the integration of simulation and optimization for the design and improvement of facility layouts taking into account production and logistics constraints. The paper includes a generic perspective and a detailed implementation. The proposed methodology is evaluated in two case studies and by drawing on the principles and tools of the functional resonance analysis method. This method analyzes the implementation order and variability of a group of processes that can lead to unwanted outcomes. The results can provide managers and other stakeholders with a methodology that adequately considers production and logistics constraints when seeking an optimized facility layout design.

In the Industry 4.0 era, advanced analytical tools are essential for progressing with digital transformation, especially within complex socio-technical systems. However, the growing complexity of these systems in manufacturing impedes system improvement, and traditional analytical methods focusing solely on the technological aspect often fall short. To overcome this problem, this paper introduces an integrated methodology combining Discrete-Event Simulation, Functional Resonance Analysis Method, and Work Domain Analysis for analysing and enhancing manufacturing systems by considering factors like operator skill levels, demand changes, and production constraints. Implemented in two industrial case studies, this methodology effectively identifies system limitations and aids in structured data analysis, positioning it as a vital decision support system in the digital transformation of Industry 4.0. 

With the emergence of Industry 5.0 and an increasing focus on human-centric approaches in manufacturing, the analysis of workers in production systems has gathered significant interest among researchers and practitioners. Previous studies have explored the impact of various aspects, such as skills, fatigue, and circadian rhythms, on human performance. However, the cumulative effect of these aspects as disturbances on work performance has yet to be fully elucidated. This study introduces an approach using the Functional Resonance Analysis Method (FRAM) to investigate the impact of multiple disturbances on workers’ performance. Furthermore, this approach explored how the resilience-related skill aspects of workers affect their performance under multiple disturbances. A case study on engine test and repair processes was conducted, employing qualitative data collection and semi-quantitative simulation studies examining the impact of combined disturbances across 4,094 scenarios. The results show that a larger number of compounded variabilities expressed in Common Performance Conditions (CPCs) made it significantly challenging to recover work performance, and CPCs with particularly critical effects were identified. In addition, the FRAM model of skilled workers was shown to sustain higher performance across more scenarios. The approach of this study has demonstrated its ability to provide insights for effectively and safely managing production systems while considering complex disturbances.

With robotics applications playing an increasingly significant role in our daily lives, it is crucial to develop effective methods for teaching and understanding their behavior. However, limited access to physical robots in educational institutions and companies poses a significant obstacle for many individuals. To overcome this barrier, a novel framework that combines realistic robot simulation and intuitive control mechanisms within a virtual reality environment is presented. By accurately emulating the physical characteristics and behaviors of various robots, this framework offers an immersive and authentic learning experience. Through an intuitive control interface, users can interact naturally with virtual robots, facilitating the acquisition of practical robotics skills. In this study, a qualitative assessment to evaluate the effectiveness and user satisfaction with the framework is conducted. The results highlighted its usability, realism, and educational value. Specifically, the framework bridges the gap between theoretical knowledge and practical application in robotics, enabling users to gain hands-on experience and develop a deeper understanding of robot behavior and control strategies. Compared to existing approaches, the framework provides a more accessible and effective alternative for interacting with robots, particularly for individuals with limited physical access to such devices. In conclusion, the study presents a comprehensive framework that leverages virtual reality technology to enhance the learning and training process in robotics. By combining realistic simulations and intuitive controls, this framework represents a significant advancement in providing an immersive and effective learning environment. The positive user feedback obtained from the study reinforces the value and potential of the framework in facilitating the acquisition of essential robotics skills. Ultimately, this work contributes to flattening the robotics learning curve and promoting broader access to robotics education.