Understanding Planning Tasks
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Understanding Planning Tasks
Author: Malte Helmert
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-01-24
This monograph is a revised version of Malte Helmert's doctoral thesis, Solving Planning Tasks in Theory and Practice, written under the supervision of Professor Bernhard Nebel at Albert-Ludwigs-Universität Freiburg, Germany, in 2006. The book contains an exhaustive analysis of the computational complexity of the benchmark problems that have been used in the past decade. Not only that, but it also provides an in-depth analysis of so-called routing and transportation problems.
Understanding Planning Tasks
This monograph is a revised version of Malte Helmert's doctoral thesis, Solving Planning Tasks in Theory and Practice, written under the supervision of Professor Bernhard Nebel at Albert-Ludwigs-Universität Freiburg, Germany, in 2006. The book contains an exhaustive analysis of the computational complexity of the benchmark problems that have been used in the past decade. Not only that, but it also provides an in-depth analysis of so-called routing and transportation problems.
Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms
Author: Sandra Lechtenberg
language: en
Publisher: Logos Verlag Berlin GmbH
Release Date: 2023-10-26
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.