Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization

Fuente: arXiv
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Autores principales: Amiri, Saeid, Zehtabi, Parisa, Dervovic, Danial, Cashmore, Michael
Formato: Preprint
Publicado: 2024
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author Amiri, Saeid
Zehtabi, Parisa
Dervovic, Danial
Cashmore, Michael
author_facet Amiri, Saeid
Zehtabi, Parisa
Dervovic, Danial
Cashmore, Michael
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_09925
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Amiri, Saeid
Zehtabi, Parisa
Dervovic, Danial
Cashmore, Michael
Artificial Intelligence
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.
title Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
topic Artificial Intelligence
url https://arxiv.org/abs/2403.09925