  Room allocation is a challenging task in detention centers since lots of
related people need to be held separately with limited rooms. It is extremely
difficult and risky to allocate rooms manually, especially for organized crime
groups with close connections. To tackle this problem, we develop an
intelligent room allocation system for detention centers to provide optimized
room allocation schemes automatically. We first formalize the detention room
allocation problem as inverse graph partitioning, which can measure the quality
of room allocation schemes. Then, we propose two heuristic algorithms to
achieve the global optimization and local optimization of detention room
allocation. Experiment results on real-world datasets show that the proposed
algorithms significantly outperform manual allocation and suggest that the
system is of great practical application value.
