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Development of a simulation-based solution concept for AI-driven clustering / combination of pick and stow operations to improve logistics performance in SMEs
No full textThe industrial sector is evolving towards increased customization, diminishing batch sizes, and shorter product lifecycles, affecting intralogistics, which faces challenges in managing an expanding variety of parts and variants. This diversification leads to a decline in efficiency owing to the complexity in pick and stow operations, as traditional systems, digital solutions, and optimization methods mainly rely on historical data without incorporating near-real-time process information. Conventional approaches separate pick and stow operations in both process and workforce, culminating in extended process durations. Instead, data-driven AI-based methods offer a solution by clustering and combining pick and stow operations into optimized bundles, considering travel distance and time. The research employs AI algorithms to streamline picking and stowing, aiming to enhance logistics performance by reducing travel distance and time. Due to the absence of real data, a simulation-based procedure to generate synthetic test and training data is adopted. The real-world logistics system of the learning factory Werk150 is modeled in AnyLogic simulation software to carry out picking and stowing in a 3D warehouse layout. This database is leveraged to train an unsupervised machine learning model using the data analytics software TensorFlow by applying algorithms focused on clustering and combination. A comparative study of these algorithms is conducted to pinpoint optimal strategies for improving logistics performance. Future research will target this methodology, which will be enriched by experimental tests in Werk150 involving near-real-time data, practical investigations, and the use of real data to conclude with an analysis to validate the optimization strategies' effectiveness
Fundamental differences between analog and digital design problems : an introduction
No full textThis article discusses fundamental differences between analog and digital circuits from a design perspective. On this basis one can understand why the design flows of these two circuit types differ so greatly, notably with regard to their degree of automation
Novel approach for the preparation of a highly hydrophobic coating material exhibiting self-healing properties
Get PDFA concept to prepare a highly hydrophobic composite with self-healing properties has been designed and verified. The new material is based on a composite of a crystalline hydrophobic fluoro wax, synthesized from montan waxes and perfluoroethylene alcohols, combined with spherical silica nanoparticles equipped with a hydrophobic shell. Highly repellent layers were prepared using this combination of a hydrophobic crystalline wax and silica nanoparticles. The novel aspect of our concept was to prepare a ladder-like structure of the hydrophobic shell allowing the inclusion of a certain share of wax molecules. Wax molecules trapped in the hydrophobic structure during mixing are hindered from crystallizing; therefore, these molecules maintain a higher mobility compared to crystallized molecules. When a thin layer of the composite material is mechanically damaged, the mobile wax molecules can migrate and heal the defects to a certain extent. The general preparation of the composite is described and XRD analysis demonstrated that a certain share of wax molecules in the composite are hindered to crystallize. Furthermore, we show that the resulting material can recovery its repellent properties after surface damage
Community-based propagation to scale up educational innovations in sustainability
Get PDFMany high-quality educational innovations are freely available, and some are known to motivate evidence-based climate and sustainability action. Typically, eforts to propagate educational innovations rely on outreach and word-of-mouth difusion, but these approaches tend to achieve little. We develop and analyse a dynamic computational model to understand why and to test other propagation strategies. Our analysis reveals that outreach has limited impact and does little to accelerate word-of-mouth adoption under conditions typical in higher education. Instead, we fnd that community-based propagation can rapidly accelerate adoption, as is also shown by a small number of successful real-world scaling eforts. This approach supports a community of ‘ambassadors’, facilitating and rewarding their sharing the innovation with potential adopters. Community-based propagation can generate exponential growth in adopters, rapidly outpacing outreach and word-of-mouth propagation. Without it, we are unlikely to rapidly scale the educational innovations needed to build urgently needed capacity in sustainability
Job insecurity and innovative behavior : the mediating role of impression management and the moderating role of job embeddedness
No full textBased on Conservation of resources (COR) theory and job preservation motivation, this paper examines the mediating role of impression management between job insecurity and employees’ innovative behavior, and the moderating role of job embeddedness in the process. Using two-wave data from 315 samples obtained through the Credamo platform in China, the indirect effects of quantitative and qualitative job insecurity on employees’ innovative behavior were found to be different and to be mediated by defensiveness and assertiveness in impression management. Moreover, job embeddedness moderated the relationship not only between job insecurity and impression management, but also between job insecurity and innovative behavior via impression management in moderated mediation analyses. This study provides new insights into the mechanism between job insecurity and innovative conduct from the impression management perspective
Integrating quantum computing into smart maintenance scheduling problems
Get PDFThis paper investigates the integration of quantum computing into smart maintenance, which allows integrated scheduling of maintenance and production to enhance decision-making within manufacturing environments. Uncoordination and the lack of integrated scheduling of maintenance and production plans lead to significant economic inefficiencies. A literature review revealed the gap in the integration of modern and newly emerging quantum computing algorithms and a three-step optimization approach is proposed. The paper showcases the feasibility of quantum computing for smart maintenance scheduling and illustrates a way of including quantum computing in complex integrated scheduling problems. The approach encompasses creating an integrated production and maintenance schedule via simulation-based optimization and metaheuristics and applying the quantum approximate optimization algorithm for prescheduling maintenance tasks
Interleaved asynchronous delta-sigma modulation concept for dynamic power converters
No full textThis paper proposes a novel synchronization concept for asynchronous delta-sigma modulators (ADSM), enabling its application for interleaved operation of parallel power converter structures. Two separate first-order ADSMs are coupled via a phase-locked loop, ensuring constant phase relationship between the modulators across their switching frequency ranges. Thus, the benefits of delta-sigma modulation such as spread spectrum qualities and lower average switching frequencies compared to pulse-width modulation can be used in dynamic interleaved power converters. In this paper, we derive system equations for the synchronization loop and confirm its functionality by simulations and measurements for a two-phase interleaved half-bridge
Business success in Africa : academic and managerial insights
No full textThis book combines academic and managerial insights on achieving business success in Sub-Saharan African markets. It offers a holistic view of business in Africa by addressing key elements of a business model. After a part that presents situational analyses of the business environment companies encounter in Africa, the book delves into the operational analysis. Each section is introduced by a conceptual chapter written by academics to set the stage and provide an overview of the pertinent issues for the subject matter in Africa. Subsequent chapters written by managers offer in-depth insights into some of the specific issues, challenges, and actions companies engage in while doing business in Africa. The sections cover market entry forms, sales, marketing and distribution, supply chain and logistics, as well as leadership, human resources and financing. The book brings together the European and the African perspectives as operational business issues are equally relevant for all companies. The authors come from a wide range of countries, from the USA to Europe and Africa, and cover a multitude of industries. The book is appropriate for both European and African practitioners and scholars
Development of a digital transformation roadmap for urban water supply utilities
No full textThis paper provides strategic guidance through the development of a structured and systematic roadmap to support urban water supply utilities with the process of digital transformation. The roadmap design requirements are established and refined through a state-of-the-art literature review and empirical investigation utilizing semi-structured interviews with subject matter experts. The research identifies seven distinct roadmap phases to be completed in an agile, continuous, and iterative manner to better navigate the complex landscape of digital transformation. In particular, each of the respective roadmap phases is characterized by distinct objectives and equipped with practical tools enabling water supply utilities to exploit the potential of digital transformation according to their individual requirements and characteristics. Finally, the roadmap is verified and substantiated by performing face validation through semi-structured interviews
UV hyperspectral imaging with xenon and deuterium light sources: integrating PCA and neural networks for analysis of different raw cotton types
Get PDFUltraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225–408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy. Principal component analysis (PCA) and Quadratic Discriminant Analysis (QDA) were employed to differentiate between various cotton types and hemp plant. PCA for the XBO illumination revealed that the first three principal components (PCs) accounted for 94.8% of the total variance: PC1 (78.4%) and PC2 (11.6%) clustered the samples into four main groups—hemp (HP), recycled cotton (RcC), and organic cotton (OC) from the other cotton samples—while PC3 (6%) further separated RcC. When using the deuterium light source, the first three PCs explained 89.4% of the variance, effectively distinguishing sample types such as HP, RcC, and OC from the remaining samples, with PC3 clearly separating RcC. When combining the PCA scores with QDA, the classification accuracy reached 76.1% for the XBO light source and 85.1% for the deuterium light source. Furthermore, a deep learning technique called a fully connected neural network for classification was applied. The classification accuracy for the XBO and deuterium light sources reached 83.6% and 90.1%, respectively. The results highlight the ability of this method to differentiate conventional and organic cotton, as well as hemp, and to identify distinct types of recycled cotton, suggesting varying recycling processes and possible common origins with raw cotton. These findings underscore the potential of UV hyperspectral imaging, coupled with chemometric models, as a powerful tool for enhancing cotton classification accuracy in the textile industry