等预算下MCTS与随机重启列表调度对比：受控计算研究
原标题：Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
AI 摘要
该研究在受控合成域中对MCTS与随机重启列表调度进行等预算比较，所有方法获得相同数量的候选调度评估。在2048次评估的主预算下，MCTS比随机重启差2.88%，且700/720的MCTS首次结果仅为其热启动。将选择规则换为教科书式UCT后差距大部分消失但未反转，结果限于小规模合成任务，未声称更大预算下的结论。
正文节选
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study Abstract Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that could refute it—giving a much simpler method the same search effort. We run that comparison under a controlled protocol. All arms receive an identical budget—the same number of candidate schedules