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

• 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
• Institution: Singapore Management University

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...