Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms

Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms
Author :
Publisher : Logos Verlag Berlin GmbH
Total Pages : 364
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ISBN-10 : 9783832556303
ISBN-13 : 3832556303
Rating : 4/5 (303 Downloads)

Book Synopsis Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms by : Sandra Lechtenberg

Download or read book Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms written by Sandra Lechtenberg and published by Logos Verlag Berlin GmbH. This book was released on 2023-10-26 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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