This paper aims to optimize placement/allocation of mobile shelter hospitals in anticipation of demand for example, during mass emergencies. It introduces a network model, graphical games, studies nash equilibrium of the game, and uses multi-armed bandit learning algorithms to find it.
本文旨在优化移动避难医院的位置/分配，以应对例如大规模紧急情况下的需求。它引入了一个网络模型，图形游戏，研究了游戏的纳什均衡，并使用多臂赌博学习算法来找到它。

(+) Optimal placement/allocation problem of mobile hospitals is an important problem. It is not too different from the ambulance location problem which is very well studied in the OR literature.
移动医院的最佳位置/分配问题是一个重要的问题。它与救护车位置问题并没有太大的区别，在运筹学文献中已经得到了很好的研究。

(-) There are a number of problems with this paper: The first is modeling - it is unrealistic and doesn't really make much sense. A lot of stuff is thrown around - queues, graphs, games; Nash equilbria, multi-armed bandit eq. And it is hard to understand the relevance in the mish-mash.
这篇论文存在一些问题：首先是建模，它不切实际，也没有太多意义。很多东西被扔进来 - 队列、图表、游戏；纳什均衡、多臂赌博机等等。很难理解这些混乱中的相关性。

(-) There are some experimental results: One would expect at least some aspect of it to come from a real world setting/data. But it is all synthetic.
有一些实验结果：人们期望至少有一些方面来自真实世界的环境/数据。但是这全部都是合成的。

Overall, a poor paper that should have received a desk reject instead of wasting reviewer time.
总体而言，这是一篇糟糕的论文，本应该被直接拒稿，而不是浪费审稿人的时间。

1. Please clarify why strategic aspect is being considered, why Nash eq. is a suitable notion to predict outcome and how Algorithm 1 is relevant.
   请澄清为什么要考虑战略方面，为什么纳什均衡是一个合适的概念来预测结果，以及算法1的相关性。

N/A

This paper presents an interesting approach to optimizing the layout and allocation of medical resources in mobile shelter hospitals during emergency situations. The authors model the hospital network as a directed hypergraph and formulate the optimization problem of minimizing total service time while accounting for patient flows, queues, priorities, and randomness in arrivals and routes.
本文提出了一种有趣的方法，用于在紧急情况下优化移动救护医院的布局和医疗资源分配。作者将医院网络建模为有向超图，并制定了最小化总服务时间的优化问题，同时考虑了患者流动、队列、优先级以及到达和路线的随机性。

First of all, there is a pure application paper with no new methodology or theory. The problem is well-motivated and the introduction of the real world application is clear.
首先，这是一篇纯粹的应用论文，没有新的方法或理论。问题的动机很好，对真实世界应用的介绍很清晰。

1. All the experiments are synthetic. Medical unit allocation is an important problem but without using any real data, it is very hard to know if the simulator is realistic. I do not think we can use this simple simulator to model the real world situation with patient-hospital interaction. Then the claim of utilization rate / treating time / x% of improvement is meaningless since we can easily tune the simulator parameter. As this is a pure applied paper, I think working with real data is required.
   所有的实验都是合成的。医疗单位分配是一个重要的问题，但是如果没有使用任何真实数据，很难知道模拟器是否真实可靠。我认为我们不能使用这个简单的模拟器来模拟真实世界中的患者-医院互动情况。因此，利用率/治疗时间/改善的x%的声明是没有意义的，因为我们可以轻松调整模拟器参数。由于这是一篇纯应用的论文，我认为需要使用真实数据。

2. I do not see the necessary of using multi-armed bandit approach. Will the goal be to solve the optimization problem (1)? Is that a pure optimization problem? Bandits try to solve exploration problem. Why in this application we need exploration? To explore what? In bandits, there are two objectives: one is cumulative regret minimization and second one is best-arm identification. It is better to clarify which formulation you are using. Different problem formulation have different metrics to use.
   我不认为使用多臂赌博机方法是必要的。目标是解决优化问题（1）吗？这是一个纯粹的优化问题吗？赌博机试图解决探索问题。为什么在这个应用中我们需要探索？探索什么？在赌博机中，有两个目标：一个是累积遗憾最小化，另一个是最佳臂识别。最好澄清你使用的是哪种公式。不同的问题公式有不同的度量指标。

3. I do not think this paper has anything to do with reinforcement learning. There is no state concept at all. Bandits do not belong to RL.
   我不认为这篇论文与强化学习有任何关系。根本没有状态的概念。赌徒问题不属于强化学习。

see above 请参见上文

see above 请参见上文

This paper proposes an approach to optimizing medical unit allocation in mobile shelter hospitals by integrating graphical game theory and reinforcement learning, specifically through the use of Nash equilibrium and the Multi-Armed Bandit (MAB) algorithm. It models the patient flow within a hospital as a directed graph, considering randomness in patient arrivals and movements, to improve operational metrics such as total treatment time, room utilization rate, and the reduction of lingering patient numbers. The approach is validated via a simulation involving scenarios with 3,000 patients, demonstrating its potential to enhance the efficiency and responsiveness of mobile shelter hospitals in complex medical scenarios.
本文提出了一种在移动救护医院中通过整合图形博弈论和强化学习来优化医疗单元分配的方法，具体包括使用纳什均衡和多臂赌博机（MAB）算法。它将医院内的患者流动建模为有向图，考虑患者到达和移动的随机性，以改善总治疗时间、房间利用率和减少滞留患者数量等运营指标。通过涉及3,000名患者的模拟验证了该方法的潜力，展示了它在复杂医疗场景中提高移动救护医院效率和响应能力的能力。

Strengths: The study addresses a critical and timely issue, offering the potential for a significant impact on emergency medical response. The findings could be beneficial for the planning and operation of mobile shelter hospitals, especially in disaster response scenarios. The use of Nash equilibrium to model patient strategies in seeking medical services is novel in the context of mobile shelter hospitals.
优势：该研究涉及一个关键且及时的问题，有可能对紧急医疗救援产生重大影响。研究结果对移动救护医院的规划和运营，特别是在灾难响应场景中，可能会有益处。在移动救护医院的背景下，使用纳什均衡模型患者寻求医疗服务的策略是新颖的。

Weaknesses: The approach builds upon existing theories and algorithms. The uniqueness lies more in the application than in the development of new methodologies.
弱点：该方法建立在现有的理论和算法基础上。其独特之处更多地体现在应用方面，而不是开发新方法的方面。

Some parts of the optimization objective and the implementation details could be elaborated further for clarity.
一些优化目标和实施细节可以进一步详细阐述以提高清晰度。

The connection between the proposed method and the resolution of the defined optimization problem and game structure is not convincingly established. It remains unclear how the method directly addresses the optimization of medical unit allocation in the context of the game theoretical model presented and how it contributes to finding an equilibrium in patient strategies.
所提出的方法与定义的优化问题和博弈结构之间的联系尚未令人信服地建立起来。目前还不清楚该方法如何直接解决在所提出的博弈理论模型下的医疗单位分配优化问题，并且它如何有助于找到患者策略的均衡。

More explicit details about the problem space, including constraints and assumptions made in the model are not stated explicitly.
问题空间的更明确细节，包括在模型中所做的约束和假设，并没有明确说明。

What is t_v in the second row of Eq.(1)? Is it the servicing time t_v^s? And what is this objective maximizing over? Is it the servicing time of a particular clinic room v, the minimum across all clinic rooms, or the sum of all?
在方程（1）的第二行中，t_v是什么？它是服务时间t_v^s吗？这个目标是在什么上最大化？是特定诊所房间v的服务时间，所有诊所房间的最小值，还是所有诊所房间的总和？

In lines 256 and 257, why is [O, X_6, X_1, X_3, D] not an option? Should it be X_1 instead of X_2 in line 253?
在第256行和第257行，为什么 [O, X_6, X_1, X_3, D] 不是一个选项？在第253行，应该是 X_1 而不是 X_2 吗？

How to define the ascending order of all patients? Does it depend on both arriving time and priority? Do these need to be known in advance? Would a patient arrive later but with higher priority break the equilibrium? And how to obtain the order of all patient if their arrival times are subject to a randomized distribution?
如何定义所有患者的升序排列？这取决于到达时间和优先级吗？这些需要事先知道吗？如果一个患者到达时间较晚但优先级较高，会打破平衡吗？如果患者的到达时间服从随机分布，如何获得所有患者的顺序？

What are the potential challenges in implementing this model in a live hospital setting, and how might they be addressed?
在实施这个模型的医院现场环境中可能面临哪些潜在挑战，以及如何解决这些挑战？

While the paper acknowledges the simulation-based nature of its findings, a discussion on the limitations regarding real-world applicability and potential negative societal impacts is somewhat lacking. The paper might benefit from additional validation, such as real-world testing or comparison with other optimization techniques.
尽管该论文承认其研究结果基于模拟，但对于实际应用的局限性和潜在的负面社会影响的讨论还有些不足。该论文可能会受益于额外的验证，例如实际测试或与其他优化技术的比较。