# Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization

arXiv: https://arxiv.org/abs/2403.09925 · HTML: https://mlsanity.com/paper/2403.09925/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems](https://mlsanity.com/paper/2104.01646/) (2021)
- [Surrogate Assisted Optimisation for Travelling Thief Problems](https://mlsanity.com/paper/2005.06695/) (2020)
- [A decision problem for bank branch site selection: A GIS Mapping perspective with Maximal Covering Location Problem: A case study of Isfahan, Iran](https://mlsanity.com/paper/1909.00130/) (2019)

## Built on by (most cited first, 1 of 1)

- [AlphaTransit: Learning to Design City-scale Transit Routes](https://mlsanity.com/paper/2605.28730/) (2026)

## Abstract (arXiv)

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.