Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms
Advances in Information Systems and Management Science, Bd. 69
349 pages, year of publication: 2023
price: 50.00 €
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.
Sandra Lechtenberg works as a research assistant at the European Research Center for Information Systems (ERCIS), University of Münster. She earned both her bachelor’s and master’s degrees in information systems at the same institution, with a specialization in data analytics and logistics. She also integrated these fields in her doctoral research conducted at the Chair for Information Systems and Supply Chain Management, successfully completing her PhD in January 2023.