越览（96）——精读期刊论文的3兰德火灾项目（2）

摘要：This post will introduce the RAND fire project (2) 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（96）——精读期刊论文

《Improving emergency responsiveness with

management science》的3兰德火灾项目（2）”。

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Today, the editor brings you the

＂Yue Lan (96)：Intensive reading of the journal article＇Improving emergency responsiveness with

management science: 3 RAND fire project (2)＇＂.

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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（2）。

This post will introduce the RAND fire project (2) 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）

本节详细记录了兰德公司团队在纽约市消防项目中的贡献，以及该项目在资源分配优化、模型应用与推广、管理科学发展方面的影响。

This section documents in detail the RAND team's contributions to the New York City Fire Project and the project's impact on optimizing resource allocation, model application and promotion, and the development of management science.

兰德公司团队的研究帮助纽约市优化消防资源，关闭低效的消防公司，节省了运营成本，同时缓解了高火灾地区的工作压力。动态重新定位算法成功纳入了消防部门的管理系统，提高了调度效率。在 1974 年预算危机中，团队利用兰德模型帮助确定对消防影响最小的公司关闭方案，到1978 年共关闭了 24 家公司。尽管社区和工会反对，但强有力的分析基础支持了这些决策并击败了诉讼。

The RAND team’s research helped the City optimize firefighting resources and close inefficient fire companies, saving operating costs while relieving pressure on high-fire areas. The dynamic repositioning algorithm was successfully incorporated into the fire department’s management system, improving dispatch efficiency. During the 1974 budget crisis, the team used the RAND model to help determine company closure options with the least impact on firefighting, and a total of 24 companies were closed by 1978. Despite community and union opposition, a strong analytical basis supported these decisions and defeated the lawsuit.

接下来介绍了消防站选址模型被用于分析警报箱和消防站的响应时间，并通过静态数据优化消防资源分布。模型受到预算、政治等多种因素的影响，但仍是决策核心工具。尽管团队提出了改进消防队执勤与报警峰值匹配的方案，但工会的政治阻力使这些建议无法实施，暴露了实际操作中的限制。

Next, the fire station location model was introduced to analyze the response time of alarm boxes and fire stations, and optimize the distribution of fire resources through static data. The model is affected by many factors such as budget and politics, but it is still a core decision-making tool. Although the team proposed a plan to improve the matching of fire brigade duty and alarm peak, the political resistance of the union made these suggestions impossible to implement, exposing the limitations of actual operation.

最后作者提到了兰德公司团队在管理科学和运筹学领域发表了约 15 篇论文，并获得了多个奖项。然而，由于论文偏应用性和操作性，部分研究未被顶级期刊接受。通过美国住房和城市发展部的支持，兰德公司团队将研究推广至其他城市，并编写了系统化的分析课程书籍。这些研究成果对其他城市的消防部署分析产生了深远影响。

Finally, the author mentioned that the RAND team published about 15 papers in the field of management science and operations research and won multiple awards. However, due to the application and operational nature of the papers, some of the research was not accepted by top journals. With the support of the U.S. Department of Housing and Urban Development, the RAND team promoted the research to other cities and wrote a systematic analysis course book. These research results have had a profound impact on the fire deployment analysis of other cities.

四、知识补充（Knowledge supplement）

区间二元语言变量是一种结合模糊语言和区间数学的变量形式，用于表达带有不确定性的信息。它在二元语言变量的基础上进一步引入区间概念，能够更灵活地表示模糊性和不确定性。

Interval binary language variables are a variable form that combines fuzzy language and interval mathematics, and are used to express information with uncertainty. It further introduces the concept of interval on the basis of binary language variables, and can express fuzziness and uncertainty more flexibly.

（一）基本概念（Basic concepts）

A bigram language variable consists of a language phrase s and an attached Confidence value h, usually written as (s,h), where:

s：语言术语（如“高”、“中”、“低”）；

s: linguistic terms (e.g., “high,” “medium,” “low”);

h：置信度，表示对语言术语的信任程度，取值范围为 [0,1]。

h: confidence, indicating the degree of trust in the language term, with a value range of [0,1].

区间二元语言变量通过将置信度扩展为一个区间，允许更宽泛的表达不确定性。其形式为：

Interval bigrams allow for a broader expression of uncertainty by expanding the confidence level into an interval. Its form is:

（二）特点（Features）

1. 语言术语与置信度结合：语言术语表达定性信息（例如“重要”、“一般”）。置信度区间表达对术语的可信程度范围，反映了评估者的不确定性或模糊性。

1. Combining linguistic terms with confidence: Linguistic terms convey qualitative information (e.g., “important,” “average”). Confidence intervals express a range of confidence in the terms, reflecting the uncertainty or ambiguity of the evaluator.

2. 区间形式更灵活：通过区间表示，可以捕捉单一数值置信度无法反映的不确定性，适合复杂决策场景。

2. The interval form is more flexible: The interval representation can capture the uncertainty that cannot be reflected by a single numerical confidence level, which is suitable for complex decision-making scenarios.

3. 表达模糊性和不确定性：比传统语言变量更适合用于不确定性较大的问题，如风险评估、多属性决策等。

3. Expressing fuzziness and uncertainty: It is more suitable than traditional language variables for problems with greater uncertainty, such as risk assessment, multi-attribute decision making, etc.

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