Articoli correlati a Supporting Operational and Real-time Planning Tasks...
Supporting Operational and Real-time Planning Tasks of Road Freight Transport With Machine Learning: Guiding the Implementation of Machine Learning Algorithms: 69 - Brossura
Sinossi
World-wide trends such as globalization, demographic shifts, increased customer demands, and shorter product lifecycles present a significant challenge to the road freight transport industry: meeting the growing road freight transport demand economically while striving for sustainability. Artificial intelligence, particularly machine learning, is expected to empower transport planners to incorporate more information and react quicker to the fast-changing decision environment. Hence, using machine learning can lead to more efficient and effective transport planning. However, despite the promising prospects of machine learning in road freight transport planning, both academia and industry struggle to identify and implement suitable use cases to gain a competitive edge. In her dissertation, Sandra Lechtenberg explores how machine learning can enhance decision-making in operational and real-time road freight transport planning. She outlines an implementation guideline, which involves identifying decision tasks in planning processes, assessing their suitability for machine learning, and proposing steps to follow when implementing respective algorithms.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Compra nuovo
Visualizza questo articolo
EUR 100,61
EUR 2,00 per la spedizione da Irlanda a Italia
Destinazione, tempi e costiRisultati della ricerca per Supporting Operational and Real-time Planning Tasks...
Foto dell'editore
Supporting Operational and Real-Time Planning Tasks of Road Freight Transport with Machine Learning: Guiding the Implementation of Machine Learning . Information Systems and Management Science)
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo V9783832556303
Quantità: 2 disponibili
Immagini fornite dal venditore
Supporting Operational and Real-Time Planning Tasks of Road Freight Transport With Machine Learning : Guiding the Implementation of Machine Learning Algorithms
Da: GreatBookPrices, Columbia, MD, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo 49216477-n
Quantità: 5 disponibili
Immagini fornite dal venditore
Supporting Operational and Real-Time Planning Tasks of Road Freight Transport With Machine Learning : Guiding the Implementation of Machine Learning Algorithms
Editore:
Logos Verlag Berlin, 2023
ISBN 10: 3832556303
ISBN 13: 9783832556303
Antico o usato
Brossura
Da: GreatBookPrices, Columbia, MD, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: As New. Unread book in perfect condition. Codice articolo 49216477
Quantità: 5 disponibili
Foto dell'editore
Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning
Da: ISD LLC, Bristol, CT, U.S.A.
Valutazione del venditore 5 su 5 stelle
paperback. Condizione: New. Codice articolo 1829191
Quantità: 5 disponibili
Foto dell'editore
Supporting Operational and Real-Time Planning Tasks of Road Freight Transport with Machine Learning: Guiding the Implementation of Machine Learning . Information Systems and Management Science)
Da: Kennys Bookstore, Olney, MD, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo V9783832556303
Quantità: 2 disponibili
Foto dell'editore
Supporting Operational and Real-Time Planning Tasks of Road Freight Transport with Machine Learning (Paperback)
Editore:
Logos Verlag Berlin GmbH, Berlin, 2023
ISBN 10: 3832556303
ISBN 13: 9783832556303
Nuovo
Paperback
Da: Grand Eagle Retail, Mason, OH, U.S.A.
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. World-wide trends such as globalization, demographic shifts, increased customer demands, and shorter product lifecycles present a significant challenge to the road freight transport industry: meeting the growing road freight transport demand economically while striving for sustainability.Artificial intelligence, particularly machine learning, is expected to empower transport planners to incorporate more information and react quicker to the fast-changing decision environment. Hence, using machine learning can lead to more efficient and effective transport planning. However, despite the promising prospects of machine learning in road freight transport planning, both academia and industry struggle to identify and implement suitable use cases to gain a competitive edge.In her dissertation, Sandra Lechtenberg explores how machine learning can enhance decision-making in operational and real-time road freight transport planning. She outlines an implementation guideline, which involves identifying decision tasks in planning processes, assessing their suitability for machine learning, and proposing steps to follow when implementing respective algorithms. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9783832556303
Quantità: 1 disponibili
Foto dell'editore
Supporting Operational and Real-Time Planning Tasks of Road Freight Transport with Machine Learning (Paperback)
Editore:
Logos Verlag Berlin GmbH, Berlin, 2023
ISBN 10: 3832556303
ISBN 13: 9783832556303
Nuovo
Paperback
Da: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. World-wide trends such as globalization, demographic shifts, increased customer demands, and shorter product lifecycles present a significant challenge to the road freight transport industry: meeting the growing road freight transport demand economically while striving for sustainability.Artificial intelligence, particularly machine learning, is expected to empower transport planners to incorporate more information and react quicker to the fast-changing decision environment. Hence, using machine learning can lead to more efficient and effective transport planning. However, despite the promising prospects of machine learning in road freight transport planning, both academia and industry struggle to identify and implement suitable use cases to gain a competitive edge.In her dissertation, Sandra Lechtenberg explores how machine learning can enhance decision-making in operational and real-time road freight transport planning. She outlines an implementation guideline, which involves identifying decision tasks in planning processes, assessing their suitability for machine learning, and proposing steps to follow when implementing respective algorithms. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9783832556303
Quantità: 1 disponibili