This paper explores reoptimization techniques for solving sequences of
similar mixed integer programs (MIPs) more effectively. Traditionally, these
MIPs are solved independently, without capitalizing on information from
previously solved instances. Our approach focuses on primal bound improvements
by reusing the solutions of the previously solved instances as well as dual
bound improvements by reusing the branching history and automating
parameter-tuning. We also describe ways to improve the solver performance by
extending ideas from reliability branching to generate better pseudocosts. Our
reoptimization approach, which we developed for the computational competition
of the MIP 2023 workshop, earned us the first prize. In this paper, we
thoroughly analyze the performance of each technique and their combined impact
on the solver's performance. Finally, we present ways to extend our techniques
in practice for further improvements.Comment: Submitted to MPC journa