  We propose an ML-based model that automates and expedites the solution of
MIPs by predicting the values of variables. Our approach is motivated by the
observation that many problem instances share salient features and solution
structures since they differ only in few (time-varying) parameters. Examples
include transportation and routing problems where decisions need to be
re-optimized whenever commodity volumes or link costs change. Our method is the
first to exploit the sequential nature of the instances being solved
periodically, and can be trained with ``unlabeled'' instances, when exact
solutions are unavailable, in a semi-supervised setting. Also, we provide a
principled way of transforming the probabilistic predictions into integral
solutions. Using a battery of experiments with representative binary MIPs, we
show the gains of our model over other ML-based optimization approaches.
