Time and place
- Trial Lecture at 10.15 - 11.00 in room B-138
- Defence starts at 12.15 in room B-138
Trial lecture title:
“Practical models of vehicle routing in logistics”
Supervisors
- Professor Lars Magnus Hvattum, Faculty of Logistics, Molde University College
- Professor Swati Aggarwal, Faculty of Logistics, Molde University College
Adjudication committee
- Professor Rafael Marti Cunquero, University of Valencia, Spain
- Assistant Professor Maryam Karimi Mamaghan, Vrije Universiteit Amsterdam, The Netherlands
- Associate Professor Mohamed Ben Ahmed, Faculty of Logistics, Molde University College
Chair of defence
Professor Harald Martin Hjelle, Faculty of Logistics, Molde University College
Summary
This thesis explores how machine learning can improve methods for solving complex mathematical problems. The research demonstrates different ways machine learning can help: by learning useful patterns from data and using them to improve solutions, by working as an independent problem solver, and by automatically adjusting settings in existing solution methods to get better results. The thesis examines several applications, such as predicting when drivers deviate from their planned routes or solving a delivery grid-world problem where conditions change during planning. Overall, the findings reveal that machine learning can improve decision-making tools by providing them with useful insights learned from data.
There are two main ways computers help solve complex problems today. The first approach relies on mathematics—specifically, the field of operations research—to find high-quality solutions. However, this becomes increasingly difficult as problems grow in size and complexity. The second approach uses artificial intelligence (AI), particularly machine learning, to identify patterns in data and make intelligent predictions. Rather than treating these methods separately, this research explores how integrating machine learning into operations research can enhance decision-making. The study investigates how this hybrid approach can more effectively tackle complex optimization problems.
Anna Konovalenko (28) comes from Krolevets, Ukraine. She holds a Bachelor of Science in Informatics from Taras Shevchenko National University of Kyiv, Ukraine (2017), a Master of Science in Business Informatics, also from Taras Shevchenko National University of Kyiv, Ukraine (2019) and finally a Master of Science in Logistics Analytics, from Molde University College (2019).