越览（99）——精读期刊论文的4警局项目（1）

摘要：This post will introduce the Police station project (1) of the journal article "Improving emergency responsiveness with management

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《Improving emergency responsiveness with

management science》的4警局项目（1）”。

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＂Yue Lan (99)：Intensive reading of the journal article＇Improving emergency responsiveness with

management science: 4 Police station project (1)＇＂.

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一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍期刊论文《Improving emergency responsiveness with management science》的警局项目（1）。

This post will introduce the Police station project (1) of the journal article "Improving emergency responsiveness with management science" from three aspects: mind mapping, intensive reading content, and knowledge supplement.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

本部分讲述了RAND公司与纽约市警察局（NYPD）的合作过程，特别是在纽约城市研究院（NYCRI）成立初期的警察项目。1968年，当NYCRI开始运作时，RAND与NYPD的合作便随之启动。然而，与纽约消防局（FDNY）相比，RAND与NYPD的合作并没有那么深入，特别是在操作部署问题上的合作。最初的警察项目主要集中在较为“软性”的政策分析领域，如少数族裔招募、刑事调查的有效性以及警察腐败等问题，而非直接涉及到警察行动部署。唯一专注于部署问题的RAND团队成员是Richard Larson，他针对纽约市911紧急电话系统调度办公室提出的建议得到了及时的采纳与实施，这一点与FDNY的合作形式大为不同，后者的合作则更加强调部署和操作层面的协调。

This section describes the RAND Corporation's collaboration with the New York City Police Department (NYPD), especially the police project in the early days of the New York City Institute (NYCRI). The RAND-NYPD collaboration began in 1968 when NYCRI began operations. However, RAND's collaboration with the NYPD was not as in-depth as that with the New York Fire Department (FDNY), especially on operational deployment issues. The initial police projects focused on "softer" policy analysis areas such as minority recruitment, effectiveness of criminal investigations, and police corruption, rather than directly involving police operational deployment. The only RAND team member who focused on deployment issues was Richard Larson, whose recommendations for the New York City 911 emergency telephone system dispatch office were promptly adopted and implemented, which was very different from the FDNY collaboration, which emphasized coordination at the deployment and operational levels.

尽管如此，RAND与NYPD之间的合作并未如与FDNY的合作那样密切和顺利。NYC警察局的内外政治和人物性格在很大程度上影响了这一合作的进展。FDNY局长视RAND为盟友，并将其工作与局长自己的议程紧密联系在一起，然而在NYPD，RAND团队未能与局内的关键人物建立类似的信任与合作关系，尤其是在当时的警察局局长与RAND团队之间的关系较为紧张。局长对RAND团队的期望较高，但却未能看到团队成员能够与其预期一致地提供解决方案，尤其是在更为复杂的操作问题上。

Despite this, the collaboration between RAND and the NYPD was not as close and smooth as the collaboration with the FDNY. The internal and external politics and personalities of the NYC Police Department greatly affected the progress of this collaboration. The FDNY Commissioner viewed RAND as an ally and closely aligned its work with the Commissioner’s own agenda, but at the NYPD, the RAND team failed to establish similar trust and cooperation with key figures within the department, especially when the relationship between the then-Police Commissioner and the RAND team was tense. The Commissioner had high expectations for the RAND team, but failed to see that the team members could provide solutions consistent with his expectations, especially on more complex operational issues.

此外，NYPD在早期的工作环境中面临巨大压力。首先，警察暴力问题，尤其是对少数族裔的暴力行为，成为公众舆论的焦点，迫使NYPD面临外界的强烈批评。接着，由于Knapp委员会对警察腐败的调查揭示了许多丑闻，导致了当时警察局局长霍华德·利里的辞职。新的局长似乎认为自己掌握了解决部门问题的所有答案，整个警察局的士气低落，氛围防御性强，显然并不适合进行合作研究。RAND在此背景下的参与变得更加困难，因为NYPD的领导层缺乏合作意愿，甚至对RAND的研究成果持抵触态度。

In addition, the NYPD faced tremendous pressure in its early working environment. First, the issue of police violence, especially violence against minorities, became the focus of public opinion, forcing the NYPD to face strong criticism from the outside world. Then, the Knapp Commission's investigation into police corruption revealed many scandals, which led to the resignation of then-Police Commissioner Howard Leary. The new chief seemed to think that he had all the answers to the department's problems. The morale of the entire police department was low, and the atmosphere was defensive, which was obviously not suitable for collaborative research. RAND's participation in this context became more difficult because the NYPD's leadership lacked the willingness to cooperate and even resisted RAND's research results.

此外，RAND与市长林赛的关系也加剧了双方的紧张。林赛曾试图设立一个有效的市民监督委员会，以应对市民投诉警察暴力的问题，这一做法使得RAND被与市政府的改革政策紧密联系在一起，而这对于处于危机中的NYPD而言无疑是一个负担。在市议会的听证会上，RAND团队也遭受了强烈的批评，NYPD局长对RAND团队的成员表示不满，认为他们是“年轻的MIT毕业生”，而这些人并未能充分理解警察局的运作和需求。与FDNY的顺利合作不同，RAND团队的观点和建议在NYPD中并未得到认同，甚至被视为不合时宜的“教训”，导致了双方的合作出现了明显的分歧。

In addition, the relationship between RAND and Mayor Lindsay has also exacerbated tensions between the two sides. Lindsay once tried to set up an effective citizen oversight committee to deal with citizen complaints about police violence. This approach has closely linked RAND to the city government's reform policies, which is undoubtedly a burden for the NYPD, which is in crisis. At the City Council hearing, the RAND team also suffered strong criticism. The NYPD chief expressed dissatisfaction with the members of the RAND team, believing that they were "young MIT graduates" who did not fully understand the operation and needs of the police department. Unlike the smooth cooperation with the FDNY, the views and suggestions of the RAND team were not recognized by the NYPD, and were even regarded as untimely "lessons", leading to obvious differences in the cooperation between the two sides.

本部分描绘了RAND与NYPD合作中的诸多挑战。尽管RAND团队在某些领域提出了建设性的建议，尤其是在911系统调度的优化上，但由于NYPD内外的政治局势、领导层的抵触态度以及外部的社会环境因素，RAND的研究和建议并未能够在警察局内取得广泛的影响力。这种情形与RAND与FDNY之间的密切合作形成了鲜明对比，展示了两者之间在合作中的明显差异。

This section describes the many challenges in the cooperation between RAND and NYPD. Although the RAND team made constructive suggestions in some areas, especially in the optimization of 911 system dispatch, due to the political situation inside and outside the NYPD, the resistance of the leadership and external social factors, RAND's research and suggestions have not been able to gain widespread influence within the police department. This situation is in sharp contrast to the close cooperation between RAND and FDNY, showing the obvious differences in cooperation between the two.

四、知识补充（Knowledge supplement）

消防站选址模型是运用数学和运筹学方法，优化消防站的位置选择，以确保在发生火灾或其他紧急情况时，消防队能够在最短时间内到达事故地点并进行有效救援。这类模型通常考虑了多个因素，如地理位置、服务区域、响应时间、资源配置、成本等，以实现消防服务的最大效率。

The fire station location model uses mathematical and operational research methods to optimize the location of fire stations to ensure that the fire brigade can reach the accident site in the shortest time and carry out effective rescue in the event of a fire or other emergency. Such models usually take into account multiple factors, such as geographical location, service area, response time, resource allocation, cost, etc., to achieve the maximum efficiency of fire services.

1. 消防站选址模型的主要目标（The main objectives of the fire station site selection model）

最小化响应时间：确保消防站能够在规定的时间内到达所有受灾区域。响应时间直接影响火灾扑灭效率和伤亡人数。

Minimize response time: Ensure that fire stations can reach all disaster areas within the specified time. Response time directly affects the efficiency of fire extinguishing and the number of casualties.

最大化覆盖面积：合理分配消防站的位置，使其能够覆盖尽可能多的区域，减少服务盲区。

Maximize coverage area: Rationally allocate the locations of fire stations so that they can cover as much area as possible and reduce service blind spots.

资源优化配置：通过合理选址，优化消防资源的分配，如消防车、人员等，提升资源使用效率。

Optimal resource allocation: By selecting a reasonable site, we can optimize the allocation of fire-fighting resources, such as fire trucks and personnel, and improve the efficiency of resource utilization.

成本控制：选择合适的位置以减少建设和运营成本，确保消防站的经济性和可持续性。

Cost control: Select a suitable location to reduce construction and operation costs, ensuring the economy and sustainability of the fire station.

2. 模型类型与方法（Model types and methods）

消防站选址问题属于经典的设施选址问题的一种，通常应用以下几种模型方法来求解：

（1）整数规划模型（integer programming model）

这种方法使用整数规划来选择消防站的最佳位置，确保每个火灾发生的地点都能够在规定的时间内得到覆盖。

This approach uses integer programming to select the optimal locations for fire stations, ensuring that every fire location can be covered within the specified time.

目标函数通常是最小化总响应时间或者最大化覆盖的区域，同时约束条件包括：每个区域必须有一个消防站负责，消防站之间的距离、资源配置等。

The objective function is usually to minimize the total response time or maximize the coverage area. The constraints include: each area must have a fire station in charge, the distance between fire stations, resource allocation, etc.

（2）p-中点问题（p-midpoint problem）

p-中点问题是一个常用于设施选址的优化方法，目标是通过选择p个消防站位置，最小化所有服务点到消防站的平均距离。这个方法假设所有服务点的需求量相同，适用于较简单的选址问题。

The p-midpoint problem is an optimization method commonly used for facility location selection. The goal is to minimize the average distance from all service points to the fire station by selecting p fire station locations. This method assumes that the demand for all service points is the same and is suitable for simpler location selection problems.

（3）p-中心问题（p-Center Problem）

该模型的目标是选择p个消防站，确保任何一个地区的响应时间不超过一定的最大时间。与p-中点问题不同，p-中心问题强调的是最远距离的最小化，从而保证最远的区域能够在规定的时间内得到救援。

The goal of this model is to select p fire stations to ensure that the response time of any area does not exceed a certain maximum time. Unlike the p-midpoint problem, the p-center problem emphasizes the minimization of the farthest distance, thereby ensuring that the farthest area can be rescued within the specified time.

（4）多目标优化模型（Multi-objective optimization model）

在实际应用中，消防站选址问题往往涉及多个目标，例如最小化响应时间、最大化覆盖区域、降低建设和运营成本等。多目标优化模型可以同时处理这些相互冲突的目标，以达到最优的折中解。

In practical applications, the fire station site selection problem often involves multiple objectives, such as minimizing response time, maximizing coverage area, reducing construction and operation costs, etc. Multi-objective optimization models can handle these conflicting objectives simultaneously to achieve the optimal compromise solution.

（5）启发式与元启发式算法（Heuristics and metaheuristics）

由于消防站选址问题的规模通常较大，且具有较强的计算复杂度，传统的数学优化方法可能难以求解。在这种情况下，可以使用启发式算法（如贪心算法、模拟退火、遗传算法等）来找到近似最优解。

Since the fire station location problem is usually large in scale and has strong computational complexity, traditional mathematical optimization methods may be difficult to solve. In this case, heuristic algorithms (such as greedy algorithms, simulated annealing, genetic algorithms, etc.) can be used to find approximate optimal solutions.

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