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.

本文比较了应用Monte Carlo树搜索和数学优化两种方法解决受动态随机优化问题，基于战术野火管理的问题实例，研究了两种方法在大规模情形下对状态空间和动作空间的表现，发现在计算预算固定的情况下，MO方法在问题规模增大时胜于MCTS方法。

大规模动态资源分配的蒙特卡罗树搜索与数学优化比较