Preference learning for sustainable freight transport planning
In this project we are investigating the use of preference learning techniques over routing plans. The goal is to learn the preferences of the planners when choosing one option over another, and to more effectively reuse all of the knowledge and effort that has been put into creating previous plans.
In contrast to most research on vehicle routing today, the focus here is on intelligent tools that learn from the user directly and can hence manage and recommend different or similar routes as used in the past. This is a novel research direction and is allowing SUMY to innovate its freight transport planning process and the involvement of its employees.
Data Analytics Laboratory
Faculty of Economic and Social Sciences and Solvay Business School
Business Technology and Operations
Academic Mobility, Logistics and Automotive Technology Research Centre