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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (10): 37-45    DOI: 10.11925/infotech.2096-3467.2018.0769
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Optimizing Anti-terrorist Policing with Queueing Theory
Zhongyi Liu,Chenwang Hu(),Kun Tan,Yan Gao
School of Management, People’s Public Security University of China, Beijing 100038, China
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Abstract  

[Objective] This paper optimizes the deployment of anti-terrorist police resources based on the queueing theory, aiming to improve the effectiveness of counterterrorism actions. [Methods] First, we proposed two optimal anti-terrorist policing strategies based on the M/M/1/∞ and M/M/N/∞ queueing models. Then, we compared the performance of the two models with simulation cases on four factors. [Results] We found that the M/M/N/∞ model had better performance. [Limitations] We did not examine the proposed model with real world anti-terrorism and policing data. [Conclusions] The M/M/N/∞ queueing model could help us create better anti-terrorist policing strategies.

Key wordsQueueing Theory      Anti-Terrorist      Optimization of Police Resources     
Received: 15 July 2018      Published: 12 November 2018

Cite this article:

Zhongyi Liu,Chenwang Hu,Kun Tan,Yan Gao. Optimizing Anti-terrorist Policing with Queueing Theory. Data Analysis and Knowledge Discovery, 2018, 2(10): 37-45.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0769     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I10/37

c1 c2 n* Wq
100 300 380 0.0507
200 300 279 0.0978
300 300 234 0.1429
400 300 207 0.1864
500 300 189 0.2286
c1 c2 n* Wq
300 100 149 0.3880
300 200 197 0.2076
300 300 234 0.1429
300 400 265 0.1092
300 500 292 0.0886
c3 c2 $\frac{{{c}_{3}}}{{{c}_{2}}}$ N* 总警力(${{\bar{N}}^{\text{*}}}$) Wq
10 300 0.0333 6 120 0.01818
20 300 0.0667 6 120 0.01818
30 300 0.1000 5 100 0.09180
40 300 0.1333 5 100 0.09180
50 300 0.1667 5 100 0.09180
c3 c2 $\frac{{{c}_{3}}}{{{c}_{2}}}$ N* 总警力(${{\bar{N}}^{\text{*}}}$) Wq
30 100 0.300 5 100 0.09180
30 200 0.150 5 100 0.09180
30 300 0.100 5 100 0.09180
30 400 0.075 5 100 0.09180
30 500 0.060 6 120 0.01818
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