  This paper describes the computational challenge developed for a
computational competition held in 2023 for the $20^{\textrm{th}}$ anniversary
of the Mixed Integer Programming Workshop. The topic of this competition was
reoptimization, also known as warm starting, of mixed integer linear
optimization problems after slight changes to the input data for a common
formulation. The challenge was to accelerate the proof of optimality of the
modified instances by leveraging the information from the solving processes of
previously solved instances, all while creating high-quality primal solutions.
Specifically, we discuss the competition's format, the creation of public and
hidden datasets, and the evaluation criteria. Our goal is to establish a
methodology for the generation of benchmark instances and an evaluation
framework, along with benchmark datasets, to foster future research on
reoptimization of mixed integer linear optimization problems.
