Improving law enforcement daily deployment through machine learning-informed optimization under uncertainty

Improving law enforcement daily deployment through machine learning-informed optimization under uncertainty

Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citi...

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Bibliographic Details
Main Authors: CHASE, Jonathan David, NGUYEN, Duc Thien, SUN, Haiyang, LAU, Hoong Chuin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Law enforcement
response time
police
deployment
MITB student
Computer Sciences
Law Enforcement and Corrections
Operations Research, Systems Engineering and Industrial Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/4682
https://ink.library.smu.edu.sg/context/sis_research/article/5685/viewcontent/Law_Enforcement_Daily_Deployment_IJCAI_2019_pv.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/4682
https://ink.library.smu.edu.sg/context/sis_research/article/5685/viewcontent/Law_Enforcement_Daily_Deployment_IJCAI_2019_pv.pdf

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