越览（93）——精读期刊论文的3兰德火灾项目（1）

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

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今天小编为大家带来“越览（93）——精读期刊论文

《Improving emergency responsiveness with

management science》的3兰德火灾项目（1）”。

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Today, the editor brings you the

＂Yue Lan (93)：Intensive reading of the journal article＇Improving emergency responsiveness with

management science: 3 RAND fire 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》的兰德火灾项目（1）。

This post will introduce the RAND fire 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）

本部分首先介绍了兰德公司与纽约市消防局（FDNY）在 1968 年至 1975 年期间的合作被称为“消防项目”，被认为是其最成功的举措之一。由于火灾警报激增和消防资源停滞不前，FDNY面临着工作量过大、通信系统过时和运营成本飙升的问题。

This section begins with an account of RAND's collaboration with the New York City Fire Department (FDNY) from 1968 to 1975, known as Project Fire, which is considered one of its most successful initiatives. The FDNY was faced with an overwhelmed workload, outdated communications systems, and soaring operating costs as fire alarms proliferated and firefighting resources stagnated.

虽然最初的努力侧重于升级通信技术，但兰德公司的研究人员认为资源部署和火灾发生率预测是更关键的问题。该项目采用了运筹学和管理科学技术，并借鉴了兰德公司圣莫尼卡基地和哥伦比亚大学学者的专业知识。该团队开发了创新解决方案，为应对 FDNY 的挑战做出了重大贡献。

While initial efforts focused on upgrading communications technology, RAND researchers identified resource deployment and fire incidence prediction as more critical issues. The project employed operations research and management science techniques and drew on the expertise of scholars at RAND’s Santa Monica site and Columbia University. The team developed innovative solutions that made a significant contribution to meeting the FDNY’s challenges.

接着作者介绍到兰德公司团队进行了广泛的实地工作，清理和整理了火灾数据记录，并开发了用于消防行动的创新模拟模型。这些模型允许测试部署策略和预测警报模式，而无需进行实际实验，而这既昂贵又有风险。

The authors then describe the extensive field work that the RAND team conducted to clean and organize fire data records and develop innovative simulation models for firefighting operations. These models allow testing of deployment strategies and predictive alarm patterns without conducting actual experiments, which is both expensive and risky.

作者最后重点介绍兰德消防项目从复杂的理论模型转向更简单、更有影响力的方法，以解决更广泛的 FDNY 管理问题，例如最佳消防站位置和资源分配。“消防站选址模型”虽然简单，但却成为该项目最具影响力的成果之一。基础研究，如响应距离平方根定律的推导和测量消防车行驶速度的创新方法，提供了关键的见解。

The authors conclude by highlighting the RAND Fire Project’s move away from complex theoretical models to simpler, more impactful approaches to broader FDNY management issues, such as optimal fire station locations and resource allocation. The “Fire Station Siting Model,” though simple, became one of the project’s most influential outputs. Basic research, such as the derivation of the square root law for response distance and innovative methods for measuring fire truck travel speeds, provided key insights.

四、知识补充（Knowledge supplement）

距离平方根定律是指在某些情境下，响应距离（或平均行驶距离）与服务站点数量的平方根成反比。这一规律表明，增加服务站点的数量可以减少平均响应距离，但这种减少的幅度会随着站点数量的增加而递减。公式化表达为：

The square root law of distance states that in some situations, the response distance (or average driving distance) is inversely proportional to the square root of the number of service stations. This law indicates that increasing the number of service stations can reduce the average response distance, but the magnitude of this reduction decreases as the number of stations increases. The formula is:

在 RAND 火灾项目中，这一定律用于优化消防站的布局。研究发现，消防站的响应距离与其覆盖区域的面积有关，而面积又与站点数量的平方根成反比。因此，通过合理增加站点数量，可以有效缩短响应距离，提高服务效率。

In the RAND Fire Project, this law was used to optimize the layout of fire stations. The study found that the response distance of a fire station is related to the area of its coverage area, which is inversely proportional to the square root of the number of stations. Therefore, by reasonably increasing the number of stations, the response distance can be effectively shortened and service efficiency can be improved.

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