A Break from Tradition for the San Francisco Police: Patrol Officer Scheduling Using an Optimization-Based Decision Support System

Philip E. Taylor and Stephen J. Huxley
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Philip E. Taylor: McLaren College of Business, University of San Francisco, Ignatian Heights, San Francisco, California 94117
Stephen J. Huxley: McLaren College of Business, University of San Francisco, Ignatian Heights, San Francisco, California 94117

Interfaces, 1989, vol. 19, issue 1, 4-24

Abstract: The San Francisco Police Department (SFPD) recently implemented an optimization-based decision support system for deploying patrol officers. It forecasts hourly needs, schedules officers to maximize coverage, and allows fine tuning to meet human needs. The fine-tuning mode helps captains evaluate schedule changes and suggests alternatives. The system also evaluates policy options for strategic deployment. The integer search procedure generates solutions that make 25 percent more patrol units available in times of need, equivalent to adding 200 officers to the force or a savings of $11 million per year. Response times improved 20 percent, while revenues from traffic citations increased by $3 million per year.

Keywords: decision analysis: systems; government services: police (search for similar items in EconPapers)
Date: 1989
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