This monograph presents a comprehensive system for predictive maintenance scheduling in rental vehicle fleets, addressing the unique operational constraints of environments where the driver changes with every transaction. The work develops a multi-tier machine learning inference architecture combining per-vehicle variational autoencoder baselines for anomaly detection, an LSTM and gradient-boosted tree stacking ensemble for health index estimation with calibrated confidence intervals, and physics-informed regularization for rare failure modes. A composite maintenance scheduling priority score integrates mechanical urgency with rental-operational context, including reservation density, revenue tier, and fleet availability constraints, enabling autonomous orchestration of service events within compressed inter-rental maintenance windows. The monograph covers sensor modalities and data architectures, preprocessing and feature engineering pipelines, fleet-level demand forecasting, and data governance frameworks for connected fleet systems.
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Vitalii Kolesnykov - researcher and practitioner specializing in data-driven fleet management, predictive analytics, and intelligent transportation systems. His work focuses on the application of machine learning, sensor-based condition monitoring, and optimization methods to the operational challenges of commercial vehicle rental.
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Englisch. Bestandsnummer des Verkäufers 9786630015621
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This monograph presents a comprehensive system for predictive maintenance scheduling in rental vehicle fleets, addressing the unique operational constraints of environments where the driver changes with every transaction. The work develops a multi-tier machine learning inference architecture combining per-vehicle variational autoencoder baselines for anomaly detection, an LSTM and gradient-boosted tree stacking ensemble for health index estimation with calibrated confidence intervals, and physics-informed regularization for rare failure modes. A composite maintenance scheduling priority score integrates mechanical urgency with rental-operational context, including reservation density, revenue tier, and fleet availability constraints, enabling autonomous orchestration of service events within compressed inter-rental maintenance windows. The monograph covers sensor modalities and data architectures, preprocessing and feature engineering pipelines, fleet-level demand forecasting, and data governance frameworks for connected fleet systems. Bestandsnummer des Verkäufers 9786630015621
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware 72 pp. Englisch. Bestandsnummer des Verkäufers 9786630015621
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Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Smart Fleet Management: Data-Driven Control of Rental Vehicles | Vitalii Kolesnykov | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786630015621 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 135772299
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