Technische Universität Dresden: Qucosa
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Simulation-Based Analysis of Conflicting Objectives of Quay Cranes and Terminal Trucks at Container Terminals
No full textQuay cranes are essential operating systems at container terminals. Their scheduling involves conflicting objectives, such as minimising vessel handling times, maximising productivity, and reducing the number of required terminal trucks. Logistic Operating Curves, known from production logistics, offer a suitable method for analysing such trade-offs and can be derived and validated through simulation. This paper proposes a simulation model of a container terminal to develop and parameterise characteristic Logistic Operating Curves. The analysis focuses on the conflict between quay cranes productivity and number of terminal trucks. Different assignment procedures for terminal truck are considered to examine their impact on system productivity
AI-Driven Digital Twin Architecture for Forestry Logistics Planning
No full textForestry logistics is challenged by the stochastic nature of supply chains, with production outcomes and road accessibility highly affected by weather. Traditional planning methods, based on historical data and static heuristics, lack adaptability to real-time changes, resulting in inefficiencies. While simulation-based multi-objective optimization (SMO) offers improvements, discrete event simulation models often fall short in providing real-time decision support. This study presents an AI-driven digital twin (AI-DT) framework that transforms static SMO models into adaptive tools for harvest and transport planning. By integrating real-time data, AI, and predictive modeling, the AI-DT enables dynamic scenario analysis and responsive control of logistics. The framework enhances operational robustness, allowing decision-makers to optimize schedules under real-world constraints such as weatheraffected road conditions and uncertain supply
Multi-Case-Studie zu Barrieren und förderlichen Faktoren der digitalen Kompetenz von Patienten - Ein interdisziplinärer Ansatz
No full textDie rasanten Fortschritte digitaler Technologien haben die Gesundheitsversorgung revolutioniert und dabei unter anderem die Vernetzung von Patienten, Gesundheitsdienstleistenden, Entwicklern und Forschenden in den Mittelpunkt gerückt. Um das volle Potenzial dieser Transformation auszuschöpfen, ist es von entscheidender Bedeutung, die digitalen Kompetenzen der Patienten und des medizinischen Fachpersonals zu fördern. Besonders vor dem Hintergrund des demografischen Wandels wird das Verständnis und die Anwendung digitaler Innovationen in der Gesundheitsversorgung zu einer Schlüsselaufgabe. - Unsere Forschungsarbeit konzentrierte sich daher auf eine Multi-Case-Studie im Rahmen des „Medical Informatics Hub for Saxony“, um die für die erfolgreiche Nutzung digitaler Gesundheitstechnologien benötigten digitalen Kompetenzen von Patienten zu identifizieren. Um einen strategischen Ansatz für den Aufbau dieser Fähigkeiten entwickeln zu können, untersuchten wir zudem Einflussfaktoren auf diese Kompetenzen. - Die Ergebnisse unserer Studie verdeutlichen, dass die Digitalisierung zwar bereits im Alltag der Patienten präsent ist, Bedenken hinsichtlich der Datensicherheit jedoch ein Hemmnis für die Nutzung digitaler Innovationen darstellen. Hier spielt die Kommunikation und Interaktion mit medizinischem Fachpersonal eine entscheidende Rolle, um diese Barrieren abzubauen und dem Bedürfnis nach sozialer Interaktion im Zusammenhang mit digitaler Technologie gerecht zu werden. Die Vermittlung grundlegender Soft Skills sowohl an medizinisches Personal als auch an Patienten, eine transparente Kommunikation über Datenschutzregelungen und den Zweck freiwilliger Datenspenden sowie interdisziplinäre Schulungsprogramme, die technische Anforderungen und auch soziale Aspekte berücksichtigen, sind daher von großer Bedeutung. - Unsere Studie betont die Wichtigkeit einer einfühlsamen und individuellen ärztlichen Betreuung, um Ängste und Vorbehalte auf Patientenseite abzubauen und digitale Gesundheitstechnologien effektiv einsetzen zu können. Dieser ganzheitliche Ansatz wird entscheidend dazu beitragen, die Potenziale der Digitalisierung im Gesundheitswesen voll auszuschöpfen.The ever-expanding potential of digital technologies is increasingly influencing healthcare, with a focus on connecting various stakeholders, including patients, healthcare providers, developers, and researchers. To fully realize this potential, it is crucial to nurture and enhance the digital skills of patients, particularly considering demographic changes. Understanding and embracing digital innovations in healthcare are fundamental issues. - This study, conducted as a multi-case study within the context of the “Medical Informatics Hub for Saxony” (MiHUBx), aimed to investigate the essential digital competencies required by patients and medical professionals to effectively use digital health technologies. The study also identified influencing factors on these digital competencies, contributing to a strategic approach for developing the necessary digital skills. - Our findings reveal that digitization is becoming increasingly integrated into patients’ daily lives but concerns about data security continue to impede the adoption of digital innovations. Effective communication and interaction with medical professionals can play a pivotal role in overcoming these barriers and addressing the desire for social interaction within the realm of digital technology. - Therefore, the provision of fundamental soft skills to healthcare professionals and patients, transparent communication regarding data privacy regulations and the purpose of voluntary data contributions, and interdisciplinary training programs that consider technical requirements and social aspects equally are of paramount importance. - This study underscores the significance of providing sensitive and individualized support to alleviate fears and reservations, ultimately enabling patients to harness digital health technologies effectively
A Modern Perspective on Visualizing Medical Evacuation Chain Simulations
No full textWe present a standalone visualization tool for analysing simulation runs of the Bundeswehr’s medical evacuation chain. Traditional simulation platforms, such as AnyLogic, tightly couple simulation and visualization, limiting flexibility and analytical depth. Our tool decouples these components, enabling efficient postsimulation analysis based on logged event data. Using an efficient approach to parse and interpolate the logged data, our system quickly constructs system states at arbitrary time points, supporting smooth playback, rewinding, and timeline navigation – even in large-scale scenarios. The interface is designed according to Shneiderman’s Visual Information-Seeking Mantra, offering an overview map, zooming, filtering, object-level details, and visualization of relationships. It facilitates advanced verification and validation, and intuitive communication with
stakeholders. By enabling interactive and retrospective exploration of medical evacuation simulations, our approach enhances transparency, usability, and insight generation in complex logistics models
Learning-Based and Heuristic Coordination of Dispatch and Charging Agents in Automated Transport Systems
No full textWe propose a reinforcement-learning-based multi-agent framework for intralogistics control, featuring a dispatching agent that learns to assign orders to automated guided vehicles (AGVs) and a charging agent that optimizes battery management. In simulations across 30 benchmark scenarios, our dispatch agent reduced delivery-deadline violations by up to 68.3 % compared to a nearest-vehicle heuristic and by over 68 % relative to random dispatching, regardless of the charging strategy employed. The charging agent further improved AGV utilization and energy efficiency. While these results demonstrate the promise of end-to-end learning for holistic material-flow control, the method’s reliance on large training datasets and its scalability to very large fleets remain areas for future investigation
The effects of pre-arrival vessel prioritisation strategies for port call coordination
No full textThis simulation study investigates the impacts of pre-arrival vessel prioritization strategies on port call coordination using a simulation model that integrates discrete-event and agent-based techniques. Motivated by the environmental and operational inefficiencies of the traditional first-come-first-served (FCFS) port policies, the simulation assesses how early and structured communication of arrival intentions, as first-announced-first-served (FAFS), can enhance berth allocation, improve resource utilization in the port, and reduce ship emissions. The model replicates a real port environment using empirical data and Python-based libraries evaluating multiple prioritization strategies under varying timing rules for port call announcements. Results demonstrate that structured pre-arrival announcements improve turnaround times and berth occupancy, particularly under strategies setting upper timing limits. However, results vary by terminal type and installed capacity. The findings underscore the need for improved digital infrastructure and cooperative
governance to enable Just-in-Time (JIT) arrivals, highlighting the potential for simulation to support decision-making in port operations modernization
Simulation-Based Forecast Optimization for Sporadic Demand in Capital Goods
No full textThis paper presents a digital planning approach designed for managing sporadic material demand. It utilizes simulation-based optimization to automate both the selection and the fine-tuning of forecasting algorithms for individual items. Although accurate demand forecasting is essential for the efficient planning of spare parts inventories, existing practical solutions for irregular demand patterns remain limited. In order to address this gap, we propose a hybrid approach combining rulebased heuristics, static time series simulation, and metaheuristic parameter optimization to generate tailored demand forecasts. The approach is validated through two capital goods manufacturers, demonstrating notable enhancements in forecast accuracy, particularly for low-frequency and unpredictable demand profiles
The resource-rationality in heuristics applied by humans for planning in probabilistic environments
No full textHuman planning often relies on heuristics, i.e., planning strategies that selectively ignore parts of the environmental information to reduce cognitive costs. According to the framework of resource-rationality, such heuristics reflect adaptive strategies that optimize the trade-off between decision quality and cognitive costs within the limits of available cognitive and temporal resources humans have. Previous studies on resource-rational planning have primarily focused on deterministic environments. However, real-world planning typically involves uncertainty, requiring heuristics that are sensitive to probabilistic outcomes. This thesis investigates whether human planners apply resource-rational heuristics in probabilistic settings, how heuristic selection is influenced by individual cognitive constraints, and how heuristics are adapted to dynamic environmental features.
To address these questions, two behavioral studies were conducted. In both studies, participants performed probabilistic three-step sequential decision-making tasks. Planning was formalized within the model-based reinforcement learning framework, in which planning heuristics are modeled as selective search through a decision tree and planning costs are quantified as decision tree complexity. Bayesian inference was used to infer latent planning parameters and analyze behavior in terms of cost-efficiency and heuristic selection.
Study I assessed whether young adults (18-35 years, n = 57) and older adults (65-75 years, n = 50) reduced planning costs by focusing on the most probable branches of the decision tree while pruning, i.e. ignoring branches of lower probability (low-probability pruning). This heuristic was resource-rational in the given task environment. Additionally, the study examined whether this heuristic was additionally influenced by probability discounting - a decision bias in which outcomes are devalued as the odds against receiving them increase - potentially affecting the selection of heuristics beyond considerations of cognitive cost. Study I showed that participants of both age groups applied the resource-rational pruning heuristic and discounted probabilistic outcomes. Older adults showed stronger discounting, shallower planning depth, and increased decision noise, all of which contributed to lower planning performance compared to younger adults. The findings indicate that resource-rational heuristics are applied in probabilistic environments and that heuristic selection is influenced by age-related differences in cognitive resources and outcome evaluation.
Study II assessed how participants (20-35 years, N = 71) adapt resource-rational heuristics to varying environmental features in a novel probabilistic planning task. Again, the low-probability pruning heuristic was resource-rational but also introduced potential replanning costs when previously pruned outcomes occurred. Replanning costs were systematically manipulated to assess their effect on heuristic adaptation, specifically, whether higher costs led to reduced initial planning expenditures in terms of shallower planning depth to limit overall costs. Study II replicated the use of the resource-rational low-probability pruning heuristic in a new task environment and additionally showed that participants dynamically adjusted their planning depth: higher replanning costs led to shallower initial planning, while lower costs encouraged deeper planning. This suggests that planners regulate planning expenditures in response to environmental demands by balancing immediate and future costs. The observed behavior indicates a meta-cognitive component of resource-rational heuristic selection.
Together, both studies provide empirical evidence that human planners apply resource-rational heuristics and extend previous findings from deterministic to probabilistic environments. The findings suggest that planners estimate how the probabilistic structure of the environment influences expected outcomes and adapt their heuristics accordingly. The selection of heuristics is further adapted to changing environments through dynamic cost estimation and influenced by individual age-related differences. Moreover, this work refines current conceptualizations of planning costs by demonstrating that heuristic selection incorporates both immediate and anticipated future costs.
In addition to its theoretical and empirical contributions, this dissertation provides a methodological foundation for studying resource-rational planning in dynamic, probabilistic environments. The newly developed task design in Study II enables the systematic manipulation of planning costs, allowing for the investigation of underlying mechanisms of the selection of heuristics. Future research should build on this foundation to assess generalizable mechanisms underlying the selection of planning heuristics, incorporate more ecologically valid environments, and further investigate how meta-cognitive processes regulate the efficient allocation of cognitive resources in human planning
Accuracy and Reproducibility of AcousticTomography Significantly Increase with Precision ofSensor Position
No full textShort communication:1 Introduction 2
2 Methods and material 2
2.1 Trees 2
2.2 Tomography 2
2.3 Image analysis 3
3 Results 3
3.1 Tomograms 3
3.2 Accuracy of stem form and sensor distances 3
3.3 Time of flight 3
3.4 Areas of defects in stem cross-sections and in tomograms 3
3.5 Reproducibility 4
4 Discussion 4
References 5Acoustic tomograms are widely used in tree risk assessment. They should be accurate, repeatable andcomparable between consecutive measurements. Previous work has failed to address the effects of differentapproaches to record sensor positions, operators and models of tomograph on the resulting tomograms.In this study, three operators used the two most common sonic tomograph models to measure seven cross-sections of Norway spruce trees, which were felled after the measurement. We evaluated the effects of model,operator, and different approaches to measure sensor positions on the quality of the tomograms.The largest source of error was the position of sensors, affecting estimated stress wave velocity, the shape of thetomogram, and the size of the defect.To produce accurate and repeatable tomograms of trees with complex shapes, it is essential to measure thesensor positions precisely.:1 Introduction 2
2 Methods and material 2
2.1 Trees 2
2.2 Tomography 2
2.3 Image analysis 3
3 Results 3
3.1 Tomograms 3
3.2 Accuracy of stem form and sensor distances 3
3.3 Time of flight 3
3.4 Areas of defects in stem cross-sections and in tomograms 3
3.5 Reproducibility 4
4 Discussion 4
References
Local perceptions of the socio-demographic changes triggered by large-scale plantation forests: Evidence from rural communities in Northern Province of Sierra Leone
No full textGlobal concerns about forest sustainability have shifted the attention to plantation forests as a potential candidate to fill the wood and ecosystem service demand. In this regard, their contribution to lessening the pressure on natural forests has been recognized, and this is becoming increasingly important in the context of a changing climate. This study was designed to conceptualize the influence of large-scale plantation forests on changes in the socio-demographic characteristics in local communities. A mixed-method approach combining qualitative data from two key informant interviews with a household survey of 125 respondents was deployed to explore the local perceptions of the influence of plantation forestry on socio-demographic changes. Our results revealed mixed perceptions of the socio-demographic changes, reflecting both increasing and decreasing trends. All of the socio-demographic factors were positively influenced in a societal desired manner by plantation forestry, except household income and construction materials. The socio-demographic factors were identified as the principal determinants shaping the plantation forestry's contribution to the socio-economic development of respondents’ households. The direction of socio-demographic changes was reported to be positive across all the communities, with the magnitude of influence on the respondent's households varying between low and high. Our results suggest the need for understanding the dynamics associated with land use conversion to forest plantations in rural areas to inspire the search for options to implement an integrated landscape approach for tree plantation development with minimal social impacts on local populations