1,720,977 research outputs found
Konzeption und Realisierung eines adaptiven Referenzmodells für den Produktentstehungsprozess der Automobilindustrie aus Sicht des Projektmanagements
No full textDie hier vorliegende Arbeit hat zum Ziel, den Produktentstehungsprozess in der Automobilindustrie aus Sicht des Projektmanagements zu betrachten und diesen mit dafür geeigneten Modellierungssprachen darzustellen.
Um zunächst für den Leser ein Verständnis für das Umfeld des Produktentstehungsprozesses zu ermöglichen, wird zu Beginn der Arbeit der aktuelle Stand der Wissenschaft in Bezug auf die Geschichte, die derzeitige Situation der Automobilindustrie und die aktuellen Herausforderungen, welche es zu meistern gilt, betrachtet. Dabei ist vor allem auch ein Fokus auf die Sicht der deutschen Automobilindustrie gelegt. Hierauf aufbauend werden dann bisherige Erkenntnisse der Wissenschaft zum Produktentstehungsprozess in der Automobilindustrie dargelegt und aufgezeigt, dass auch Lieferanten hier immer stärker mit einbezogen werden.
Nachdem ein Verständnis für den automobilen Produktentstehungsprozess aufgebaut wurde, werden zudem kurz verschiedene Vorgehensmodelle innerhalb des Projektmanagements vorgestellt, welche später im Vorgehensmodell einen Einfluss ausüben. Zudem wird auch eine Abgrenzung vorgenommen, welche den Unterschied zwischen Prozess und Projekt aufgreift und im Rahmen der Arbeit definiert.
Als abschließender Teil dieses Bereichs zur existierenden Literatur, werden die verschiedenen, existierenden Modellbegriffe vorgestellt und nach welchen Grundlagen aktuell Prozessmodelle am besten erfasst werden. In diesem Kontext werden auch kurz verschiedene Modellierungssprachen für diesen speziellen Zweck vorgestellt. Zudem wird der Begriff der adaptiven Modellierung und verschiedene Adaptionstechniken zu dessen Anwendung vorgestellt. Vor allem wird auch eine Vorgehensweise vorgestellt, welche speziell dazu definiert wurde Referenzmodelle zu erstellen. Diese Vorgehensweise soll dann im eigentlichen Hauptteil der Arbeit als Leitfaden genutzt werden.
Im Abschnitt der „Methoden und Werkzeuge“ werden dann die wissenschaftlichen Grundlagen dargestellt, nach denen das Referenzmodell entwickelt wurde. Dabei bildet die Design Science das wissenschaftliche Forschungsparadigma, nach dem eine zyklische Entwicklung der Forschungsergebnisse durchgeführt wird. Ebenfalls werden wissenschaftliche Grundlagen zu Literaturrecherche, Fallstudien, Dokumentenanalyse und Interviews vorgestellt, nach denen in dieser Arbeit vorgegangen wurde.
Der eigenständige Leistung der Arbeit wird dann im Abschnitt „Konzeption und Erstellung des Referenzmodells“ beschrieben. Dabei wird zunächst dargelegt, wie im ersten Zyklus der Design Science eine Literaturrecherche und eine Dokumentenanalyse durchgeführt wurden, um aus der Fallstudie HyValue heraus Prozessmodelle für den Produktentstehungsprozess zu entwickeln. In diesem Zyklus wird ebenfalls die Auswahl der Modellierungswerkzeuge beschrieben. Als Abschluss dieses Zyklus, wurden die Modelle von Hersteller und Lieferanten mit diesen zusammen evaluiert, um die Korrektheit sicherzustellen.
Im nächsten Zyklus der Design Science wurde dann auf Basis der bisherigen Erkenntnisse das Referenzmodell aus den einzelnen Prozessmodellen entwickelt. Hierbei entstanden die in der Arbeit vorgestellten Prozessmodelle zu den einzelnen Funktionen und den kontinuierlichen Prozessen des Projektmanagements, welche durch ein Kollaborationsmodell und einen Ordnungsrahmen ergänzt werden. Zudem wird beschrieben, wie das Referenzmodell adaptiv gestaltet wurde, um abhängig vom Kontext verschiedene Variationen darstellen zu können. Um den Zyklus zu evaluieren, wurde das Referenzmodell dann Experten außerhalb der Fallstudie im Rahmen eines Interviews präsentiert.
Den Abschluss der Arbeit bildet eine Diskussion zur Einordnung der Ergebnisse für Praxis, Wissenschaft und Lehre
Resource Reservation for Time-Sensitive Vehicular Applications
No full textThis thesis investigates cost-effective and reliable resource reservation strategies for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require deterministic and guaranteed access to mobile edge computing (MEC) resources, which is typically achieved through individual reservations. Vehicles submit reservation requests to mobile network operator (MNOs), which allocate computation and communication resources based on these requests. Therefore, optimizing the timing and method for vehicles to place reservation requests in both single and multiple MNO scenarios is crucial, especially in dynamic vehicular environments with fluctuating network conditions and limited resources. Efficient reservation requests are essential as vehicles lack complete information about future resource availability and costs. Additionally, real-world uncertainties, such as unpredictable mobility, which influences future reservation times and costs, complicate the design of an optimal reservation strategy. Furthermore, dynamic pricing models employed by MNOs introduce another layer of complexity to the decision-making process for reservation requests, updates, and exchanges due to their impact on market conditions like resource supply and demand. To address the challenges of resource reservation in single MNO scenarios, this thesis proposes an advanced reservation strategy leveraging a batched long short-term memory (LSTM) model. This approach optimizes the timing of reservations, leading to significant cost savings for vehicles. To further minimize update costs, one-shot and multi-shot reservation update strategies are introduced, complemented by the heuristic greedy reservation updates (HGRU) algorithm. For multiple MNO environments, the thesis addresses cost-effective resource selection by comparing prices and network conditions across MNOs. An adaptive Markov decision process (MDP) framework is proposed, incorporating a deep reinforcement learning (DRL) algorithm, specifically dueling deep Q-learning. To enhance learning efficiency, a novel area-wise approach and adaptive MDP closely resembling real-world conditions are introduced. Furthermore, the temporal fusion transformer (TFT) is employed to effectively handle time-dependent data during model training. The multi-phase training approach, involving both synthetic and real-world data, enables the DRL agent to learn from historical data and adapt to real-time observations. Additionally, a multi-objective approach using a double deep Q-learning algorithm is proposed to minimize the cost of reservation updates while ensuring an optimal strategy and reliable provisioning. Finally, this thesis explores the use of blockchain smart contracts to establish a secure, efficient, and transparent resource trading system for vehicular networks. This blockchain-based architecture optimizes reservation costs and addresses trust issues by enabling decentralized, secure, and cost-effective resource trading among vehicles. By leveraging smart contracts, the system ensures transparency and immutability in transactions, fostering trust among participating vehicles. Simulation results demonstrate that the proposed resource reservation algorithms in single MNO environments outperform benchmark reservation schemes, including immediate reservation schemes, in terms of cost minimization and resource utilization efficiency, which also pose challenges for resource guarantee. In multiple MNO environments, the algorithms effectively manage uncertainties and promote competition between MNOs than single MNO scenario potentially impacting guaranteed resource provisioning. Additionally, while the exchange reservation strategies enhance security, the absence of robust security mechanisms could result in unreliable resource requests from providers, posing challenges to guaranteed resource provisioning
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Digitaler Zwilling in der Produktentwicklung und der Produktion
No full textA short introduction to digital twins in product development and production
Resource Reservation for Time-Sensitive Vehicular Applications
No full textThis thesis investigates cost-effective and reliable resource reservation strategies for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require deterministic and guaranteed access to mobile edge computing (MEC) resources, which is typically achieved through individual reservations. Vehicles submit reservation requests to mobile network operator (MNOs), which allocate computation and communication resources based on these requests. Therefore, optimizing the timing and method for vehicles to place reservation requests in both single and multiple MNO scenarios is crucial, especially in dynamic vehicular environments with fluctuating network conditions and limited resources. Efficient reservation requests are essential as vehicles lack complete information about future resource availability and costs. Additionally, real-world uncertainties, such as unpredictable mobility, which influences future reservation times and costs, complicate the design of an optimal reservation strategy. Furthermore, dynamic pricing models employed by MNOs introduce another layer of complexity to the decision-making process for reservation requests, updates, and exchanges due to their impact on market conditions like resource supply and demand. To address the challenges of resource reservation in single MNO scenarios, this thesis proposes an advanced reservation strategy leveraging a batched long short-term memory (LSTM) model. This approach optimizes the timing of reservations, leading to significant cost savings for vehicles. To further minimize update costs, one-shot and multi-shot reservation update strategies are introduced, complemented by the heuristic greedy reservation updates (HGRU) algorithm. For multiple MNO environments, the thesis addresses cost-effective resource selection by comparing prices and network conditions across MNOs. An adaptive Markov decision process (MDP) framework is proposed, incorporating a deep reinforcement learning (DRL) algorithm, specifically dueling deep Q-learning. To enhance learning efficiency, a novel area-wise approach and adaptive MDP closely resembling real-world conditions are introduced. Furthermore, the temporal fusion transformer (TFT) is employed to effectively handle time-dependent data during model training. The multi-phase training approach, involving both synthetic and real-world data, enables the DRL agent to learn from historical data and adapt to real-time observations. Additionally, a multi-objective approach using a double deep Q-learning algorithm is proposed to minimize the cost of reservation updates while ensuring an optimal strategy and reliable provisioning. Finally, this thesis explores the use of blockchain smart contracts to establish a secure, efficient, and transparent resource trading system for vehicular networks. This blockchain-based architecture optimizes reservation costs and addresses trust issues by enabling decentralized, secure, and cost-effective resource trading among vehicles. By leveraging smart contracts, the system ensures transparency and immutability in transactions, fostering trust among participating vehicles. Simulation results demonstrate that the proposed resource reservation algorithms in single MNO environments outperform benchmark reservation schemes, including immediate reservation schemes, in terms of cost minimization and resource utilization efficiency, which also pose challenges for resource guarantee. In multiple MNO environments, the algorithms effectively manage uncertainties and promote competition between MNOs than single MNO scenario potentially impacting guaranteed resource provisioning. Additionally, while the exchange reservation strategies enhance security, the absence of robust security mechanisms could result in unreliable resource requests from providers, posing challenges to guaranteed resource provisioning
Agilität in traditionellen Projekten
No full textScrum & Co. sind etablierte Vorgehensmodelle, die Agilität, Flexibilität und Kundenorientierung fördern. Allerdings gibt es in vielen Konstellationen nach wie vor gute Gründe, sogenannte traditionelle, planbasierte Vorgehensmodelle, wie das Wasserfall oder V-Modell einzusetzen.
In diesem Kompaktbriefing werden Methoden und Führungstechniken praxisnah vorgestellt, die Agilität auch in solchen Vorgehensmodellen ermöglichen. Es wird unterschieden zwischen agilem Mindset und Agilität fördernden Prozessen und Abläufen. Unter anderem wird der Einsatz von Kanbanboards, Stand-up Meetings, Kundenreviews sowie Kombinationen hybrider Vorgehensmodelle diskutiert. Außerdem wird versucht, das Spannungsfeld zwischen hierarchisch ausgerichteter Führung in planbasierten Projekten und Selbstorganisation und Eigenverantwortung in agilen Projekten aufzulösen.
Zielgruppe sind Projektbeteiligte, die erfahren möchten, wie traditionelle Projekte von Agilität profitieren können und welche Methoden und Techniken dies ermöglichen. Teilnehmerinnen und Teilnehmer können diese Methoden und Techniken in ihren Projekten einsetzen und verstehen Grundlagen hybrider Vorgehensmodelle sowie Herausforderungen an die Führung in hybriden Projekten
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Resource Reservation for Time-Sensitive Vehicular Applications
No full textThis thesis investigates cost-effective and reliable resource reservation strategies for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require deterministic and guaranteed access to mobile edge computing (MEC) resources, which is typically achieved through individual reservations. Vehicles submit reservation requests to mobile network operator (MNOs), which allocate computation and communication resources based on these requests. Therefore, optimizing the timing and method for vehicles to place reservation requests in both single and multiple MNO scenarios is crucial, especially in dynamic vehicular environments with fluctuating network conditions and limited resources. Efficient reservation requests are essential as vehicles lack complete information about future resource availability and costs. Additionally, real-world uncertainties, such as unpredictable mobility, which influences future reservation times and costs, complicate the design of an optimal reservation strategy. Furthermore, dynamic pricing models employed by MNOs introduce another layer of complexity to the decision-making process for reservation requests, updates, and exchanges due to their impact on market conditions like resource supply and demand. To address the challenges of resource reservation in single MNO scenarios, this thesis proposes an advanced reservation strategy leveraging a batched long short-term memory (LSTM) model. This approach optimizes the timing of reservations, leading to significant cost savings for vehicles. To further minimize update costs, one-shot and multi-shot reservation update strategies are introduced, complemented by the heuristic greedy reservation updates (HGRU) algorithm. For multiple MNO environments, the thesis addresses cost-effective resource selection by comparing prices and network conditions across MNOs. An adaptive Markov decision process (MDP) framework is proposed, incorporating a deep reinforcement learning (DRL) algorithm, specifically dueling deep Q-learning. To enhance learning efficiency, a novel area-wise approach and adaptive MDP closely resembling real-world conditions are introduced. Furthermore, the temporal fusion transformer (TFT) is employed to effectively handle time-dependent data during model training. The multi-phase training approach, involving both synthetic and real-world data, enables the DRL agent to learn from historical data and adapt to real-time observations. Additionally, a multi-objective approach using a double deep Q-learning algorithm is proposed to minimize the cost of reservation updates while ensuring an optimal strategy and reliable provisioning. Finally, this thesis explores the use of blockchain smart contracts to establish a secure, efficient, and transparent resource trading system for vehicular networks. This blockchain-based architecture optimizes reservation costs and addresses trust issues by enabling decentralized, secure, and cost-effective resource trading among vehicles. By leveraging smart contracts, the system ensures transparency and immutability in transactions, fostering trust among participating vehicles. Simulation results demonstrate that the proposed resource reservation algorithms in single MNO environments outperform benchmark reservation schemes, including immediate reservation schemes, in terms of cost minimization and resource utilization efficiency, which also pose challenges for resource guarantee. In multiple MNO environments, the algorithms effectively manage uncertainties and promote competition between MNOs than single MNO scenario potentially impacting guaranteed resource provisioning. Additionally, while the exchange reservation strategies enhance security, the absence of robust security mechanisms could result in unreliable resource requests from providers, posing challenges to guaranteed resource provisioning
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