  Deriving a good variable selection strategy in branch-and-bound is essential
for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP
branching data collected during the previous solution process, learning to
branch methods have recently become superior over heuristics. As
branch-and-bound is naturally a sequential decision making task, one should
learn to optimize the utility of the whole MIP solving process instead of being
myopic on each step. In this work, we formulate learning to branch as an
offline reinforcement learning (RL) problem, and propose a long-sighted hybrid
search scheme to construct the offline MIP dataset, which values the long-term
utilities of branching decisions. During the policy training phase, we deploy a
ranking-based reward assignment scheme to distinguish the promising samples
from the long-term or short-term view, and train the branching model named
Branch Ranking via offline policy learning. Experiments on synthetic MIP
benchmarks and real-world tasks demonstrate that Branch Rankink is more
efficient and robust, and can better generalize to large scales of MIP
instances compared to the widely used heuristics and state-of-the-art
learning-based branching models.
