  Combinatorial optimization is a well-established area in operations research
and computer science. Until recently, its methods have focused on solving
problem instances in isolation, ignoring that they often stem from related data
distributions in practice. However, recent years have seen a surge of interest
in using machine learning as a new approach for solving combinatorial problems,
either directly as solvers or by enhancing exact solvers. Based on this
context, the ML4CO aims at improving state-of-the-art combinatorial
optimization solvers by replacing key heuristic components. The competition
featured three challenging tasks: finding the best feasible solution, producing
the tightest optimality certificate, and giving an appropriate solver
configuration. Three realistic datasets were considered: balanced item
placement, workload apportionment, and maritime inventory routing. This last
dataset was kept anonymous for the contestants.
