  Industries frequently adjust their facilities network by opening new branches
in promising areas and closing branches in areas where they expect low profits.
In this paper, we examine a particular class of facility location problems. Our
objective is to minimize the loss of sales resulting from the removal of
several retail stores. However, estimating sales accurately is expensive and
time-consuming. To overcome this challenge, we leverage Monte Carlo Tree Search
(MCTS) assisted by a surrogate model that computes evaluations faster. Results
suggest that MCTS supported by a fast surrogate function can generate solutions
faster while maintaining a consistent solution compared to MCTS that does not
benefit from the surrogate function.
