Högskolan i Skövde

Occupant packaging design is usually done using computer-aided design (CAD) and digital human modelling (DHM) tools. These tools help engineers and designers explore and identify vehicle cabin configurations that meet accommodation targets. However, studies indicate that current working methods are complicated and iterative, leading to time-consuming design procedures and reduced investigations of the solution space, in turn meaning that successful design solutions may not be discovered. This paper investigates potential advantages and challenges in using an automated simulation-based multi-objective optimization (SBMOO) method combined with a DHM tool to improve the occupant packaging design process. Specifically, the paper studies how SBMOO using a genetic algorithm can address challenges introduced by human anthropometric and postural variability in occupant packaging design. The investigation focuses on a fabricated design scenario involving the spatial location of the seat and steering wheel, as well as seat angle, taking into account ergonomics objectives and constraints for various end-users. The study indicates that the SBMOO-based method can improve effectiveness and aid designers in considering human variability in the occupant packaging design process.

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

Manufacturing companies face a complex world with ever-changing demands, globalization, sustainability concerns, and economic challenges. Accurate improvement management and predicting future performance are crucial for staying competitive. Discrete-Event Simulation (DES) helps capture the dynamics of complex production systems, and Simulation-Based Optimization (SBO) can identify improvements with these models. However, past optimization approaches focused on individual production lines, which could lead to sub-optimizations when considering the performance of the manufacturing site. This research proposes a multi-level optimization approach for improvement management focusing on the performance of the manufacturing site.

High computational complexity resulting from combining several detailed models to create the site-level model is an obstacle to presenting decision support in a timeframe suitable for industrial decision-making. The research addresses this by validating a simplification method for DES models which replaces detailed models with simpler ones, sacrificing some detail and accuracy for faster runtime performance, enabling SBO for the site and supply chain level. %This is the first major contribution of this dissertation. 

The second part presents a Decision Support Architecture (DSA) using SBO to optimize site performance. The process starts by identifying the most critical bottleneck line on the entire site, including the specific parameter causing the issue, e.g., processing time or downtime. This approach prioritizes improvements with the highest impact for the least resource expenditure. Following this analysis, individual production lines are further optimized to identify specific equipment and parameters for improvement. Knowledge extraction algorithms then prioritize these improvements, guiding efforts and ensuring they benefit the entire site. Allowing for more efficient resource management, confidence that the proposed improvements are beneficial for the site, and improved decision-making at the site level.

The main novel research outcome of this dissertation lies in the multi-level optimization approach, combined with knowledge extraction and SBO enabled by simplified simulation models. This framework provides valuable insights for optimizing manufacturing sites in a complex and dynamic environment.

Dagens tillverkningsindustrier behöver konkurrera i en omvärld som präglas av föränderliga krav, globalisering, krav på hållbarhet, och ekonomiska utmaningar. Noggrann förbättringshantering och möjligheten att kunna uppskatta framtida prestanda i produktionssystemen är avgörande för att förbli konkurrenskraftiga. Discrete-Event Simulation (DES) och Simulation-Based Optimization (SBO) är kraftfulla verktyg för att modellera och optimera komplexa produktionssystem. SBO har tidigare använts främst för enskilda produktionslinjer, vilket kan leda till suboptimeringar om hänsyn tas till hela systemets prestanda. Detta vetenskapliga bidrag föreslår ett angreppsätt där flernivå-optimering nyttjas för att fokusera på resultatet för hela systemet.

För att övervinna utmaningen med långa beräkningstider för komplexa modeller, utvärderar denna forskning en metod för att förenkla DES-modeller. Genom att minska detaljnoggrannheten kan beräkningstiden reduceras, vilket möjliggör snabbare SBO-analys av hela fabriker och värdeflöden. Denna förenklingsmetod är avgörande för att kunna erbjuda beslutsstöd inom en rimlig tid för industriellt beslutsfattande. Detta bidrag utgör avhandlingens första del.

Avhandlingens andra del presenterar en beslutstödsarkitektur, kallad Decision Support Architecture (DSA), som nyttjar SBO för att optimera fabriksprestanda. Processen inleds med att identifiera den mest kritiska flaskhalsen i produktionssystemet och den specifika parametern som behöver förbättras. Den här metoden prioriterar förbättringar med störst effekt för minsta resursförbrukning. Efter denna analys optimeras varje enskild produktionslinje med detaljerade modeller för att identifiera vilken utrustning och parameter som ger störst effekt på förbättringen. Algoritmer för kunskapsutvinning används sedan för att prioritera dessa förbättringar baserat på deras effekt på hela fabrikens prestanda, vilket leder till effektivare resurshantering och förbättrat beslutsfattande på fabriksnivå.

Det största vetenskapliga bidraget från denna avhandling utgörs av den utvecklade metoden för optimering på flera nivåer i kombination med kunskapsutvinning och SBO som möjliggörs av förenklade simuleringsmodeller. Detta ramverk ger värdefulla insikter för att optimera hela tillverkningssystem i en komplex och dynamisk miljö.

Recently the concept of Industry 5.0 has been introduced, reinforcing the human-centric perspective for future industry. The human-centric scientific discipline and profession ergonomics is applied in industry to find solutions that are optimized in regard to both human well-being and overall system performance. It is found, however, that most production development and preparation work carried out in industry tends to address one of these two domains at a time, in a sequential process, typically making optimization slow and complicated. The aim of this paper is to suggest, demonstrate, and evaluate a concept that makes it possible to optimize aspects of human well-being and overall system performance in an efficient and easy parallel process. The concept enables production planning and balancing of human work in terms of two parameters: assembly time as a parameter of productivity (system performance), and risk of musculoskeletal disorders as a parameter of human well-being. A software demonstrator was developed, and results from thirteen test subjects were compared with the traditional sequential way of working. The findings show that the suggested relatively unique parallel approach has a positive impact on the expected musculoskeletal risk and does not necessarily negatively affect productivity, in terms of cycle time and time balance between assembly stations. The time to perform the more complex two-parameter optimization in parallel was shorter than the time in the sequential process. The majority of the subjects stated that they preferred the parallel way of working compared to the traditional serial way of working.

This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms.

In recent years Mixed Reality technology has been widely used to enhance operators in manual assembly operations. This paper introduces a Mixed Reality environment for assembly operations and describes how the process can be supported by virtual sensors. The structure of the environment allows seamless adaption from a fully virtual training scenario, only using virtual assets, to a full production scenario supporting operators in assembling physical products in actual production. The training system which has been developed together with the company Skandia Elevator in Sweden enables the operators to train with much less disturbance to the real production line compared to training using the actual production equipment. In fact, the training can be done only using virtual assets.

Planning and designing factory layouts are frequently performed within virtual environments, relying on inputs from various cross-disciplinary activities e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. The success of this process heavily relies on the expertise of the practitioners performing the task. Studies have shown that layout planning often hinges on the practitioners’ knowledge and interpretation of current rules and requirements. As there is significant variability in this knowledge and interpretation, there is a risk that decisions are made on incorrect grounds. Consequently, the layout planning process depends on individual proficiency. In alignment with Industry 4.0 and Industry 5.0 principles, there is a growing emphasis on providing practitioners involved in industrial development processes with efficient decision support tools. This paper presents a digital support function integrated into a virtual layout planning tool, developed to support practitioners in considering current rules and requirements for space claims in the layout planning process. This digital support function was evaluated by industry practitioners and stakeholders involved in the factory layout planning process. This initiative forms part of a broader strategy to provide advanced digital support to layout planners, enhancing objectivity and efficiency in the layout planning process while bridging cross-disciplinary gaps.

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology. 

Practitioners of multi-objective optimization currently lack open tools that provide decision support through knowledge discovery. There exist many software platforms for multi-objective optimization, but they often fall short of implementing methods for rigorous post-optimality analysis and knowledge discovery from the generated solutions. This paper presents Mimer, a multi-criteria decision support tool for solution exploration, preference elicitation, knowledge discovery, and knowledge visualization. Mimer is openly available as a web-based tool and uses state-of-the-art web-technologies based on WebAssembly to perform heavy computations on the client-side. Its features include multiple linked visualizations and input methods that enable the decision maker to interact with the solutions, knowledge discovery through interactive data mining and graph-based knowledge visualization. It also includes a complete Python programming interface for advanced data manipulation tasks that may be too specific for the graphical interface. Mimer is evaluated through a user study in which the participants are asked to perform representative tasks simulating practical analysis and decision making. The participants also complete a questionnaire about their experience and the features available in Mimer. The survey indicates that participants find Mimer useful for decision support. The participants also offered suggestions for enhancing some features and implementing new features to extend the capabilities of the tool.

The manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making.