TRAIL-VAIA Doctoral School – Predict and Optimize: combinatorial optimisation with machine-learning based inpu

In brief

Combinatorial optimisation is used to solve large scale routing, planning and scheduling problems. But how do these techniques work? And what if not all data is known, and want to integrate machine learning predictions?


In this course, we review different combinatorial optimisation problems (Vehicle Routing, Scheduling and combinatorial puzzles) as well as discrete optimisation techniques for solving them, including Branch and Bound, Integer Programming, Dynamic Programming and Multi-Valued Decision Diagrams, Constraint Programming, Local Search and Hybridization of the aforementioned techniques. A focus will be on the reusability and genericity of the techniques for solving large classes of constrained optimisation problems.



With a better understand of the solving techniques, we then move to the second part of the course which focusses on ‘Predict and Optimize’ problems, where part of the input to the combinatorial optimisation problem has to be inferred from data. We will review problems such as perception-based solving (e.g. visual sudoku), learning preferences in vehicle routing and end-to-end learning for energy-aware scheduling. We will discuss how to improve either the learning or the solving, as well as the integration of the two to obtain the best results.


Part 1 will be given by Pierre Schaus, UC Louvain, using material borrowed from his new MOOC on constraint programming: https://www.edx.org/course/constraint-programming



Part 2 will be given by Tias Guns, KU Leuven. Links will be provided to example notebooks in the CPMpy constraint modeling library: https://people.cs.kuleuven.be/~tias.guns/

Practical information

Venue: Online

Dates and times: 16 March 2023, 14:00 – 16:00

REGISTER

https://www.vaia.be/en/courses/predict-and-optimize-combinatorial-optimisation-with-machine-learning-based-input?lang=en