Supporting Operational and Real-Time Planning Tasks of Road Freight Transport with Machine Learning (Paperback)

Sandra Lechtenberg

ISBN 10: 3832556303 / ISBN 13: 9783832556303

Edité par Logos Verlag Berlin GmbH, Berlin, 2023

A propos de cet article

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.

N° de réf. du vendeur 9783832556303

Détails bibliographiques

• Titre: Supporting Operational and Real-Time Planning Tasks of Road Freight Transport with Machine Learning (Paperback)
• Éditeur: Logos Verlag Berlin GmbH, Berlin
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 3832556303
• ISBN à 13 chiffres: 9783832556303
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

Synopsis

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.