# A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation

arXiv: https://arxiv.org/abs/1405.5498 · HTML: https://mlsanity.com/paper/1405.5498/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2014.

## Built on by (most cited first, 2 of 2)

- [Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework](https://mlsanity.com/paper/2110.08423/) (2021)
- [Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization](https://mlsanity.com/paper/2407.05511/) (2024)

## Abstract (arXiv)

Dynamic resource allocation (DRA) problems are an important class of dynamic stochastic optimization problems that arise in a variety of important real-world applications. DRA problems are notoriously difficult to solve to optimality since they frequently combine stochastic elements with intractably large state and action spaces. Although the artificial intelligence and operations research communities have independently proposed two successful frameworks for solving dynamic stochastic optimization problems---Monte Carlo tree search (MCTS) and mathematical optimization (MO), respectively---the relative merits of these two approaches are not well understood. In this paper, we adapt both MCTS and MO to a problem inspired by tactical wildfire and management and undertake an extensive computational study comparing the two methods on large scale instances in terms of both the state and the action spaces. We show that both methods are able to greatly improve on a baseline, problem-specific heuristic. On smaller instances, the MCTS and MO approaches perform comparably, but the MO approach outperforms MCTS as the size of the problem increases for a fixed computational budget.