A Queuing-Linear Programming Approach to Scheduling Police Patrol Cars (original) (raw)
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Our research analyzes actual operating strategies of a public safety Emergency Response System (ERS) in a large city in Mexico integrating a sixth police district into previously published research composed of five districts out of a total of eight in the city. The research procedure firstly characterizes the demand for service and processes associated with the patrols' response and utilization during the attention of historic calls. Subsequently, we created a stochastic simulation model to emulate current ERS's patrols deployment strategies. After validating the model, we then generated a scenario with the performance's constraint of three minutes maximum patrol response time. Lastly, the minimum numbers of police back up patrols, required to provide the ideal response time for each police quadrant in every district, were obtained. Results reflect that the minimum required numbers of back up police patrols to provide an acceptable service level are viable.
Our research focuses on assisting current operations of a public safety Emergency Response System (ERS) in a large city in Mexico to achieve the international ideal response time of three minutes maximum based on allocating the optimum number of police patrols. We believe that improvements in patrol response times will strongly improve statistics in the crime prevention and control as well as in the apprehension of presumable delinquents. The city is composed by eight police districts and this research integrates an additional district to four previously evaluated. In this research we first characterized the demand for service and processes linked to the attention of the call and patrol utilization. Next, we built a stochastic simulation model to reproduce current operating conditions to validate its behavior. Ultimately, we identified the optimum number of police patrols required to be allocated as back up inventory in each police quadrant within all districts.
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Patrol car allocation model: Executive summary
This document has oeen reproduced exactly .as recei~e? from the person or organization originating it. Points of view or opinions stat~d in this document are those of the authors and do. not nec~ssanlY represent the official position or poliCies of the Nalional InstlMe of Justice.
Mexico has experienced a drastic insecurity environment in the last decade due to multiple national and international factors. In this regard, public safety Emergency Response Systems (ERS) have the potential of effectively combat and deter crime through rapid and coordinated strategies. Utilizing stochastic simulation, our research focuses on determining an ideal number of police patrols to be allocated to a public safety Emergency Response System (ERS) in order to comply with a maximum international reference response time as a strategy to deter and combat crime in a large city in Mexico. The city´s ERS is composed by eight police districts, and this research incorporates the analysis of only half of the 7 th police district to previously published results of six districts, given that this particular district is integrated by eight police quadrants, as opposed to only four adjacent quadrants found in a regular police district. Simulation scenarios include actual and proposed operating strategies of a police quadrant considering one dedicated patrol per patrolling zone plus an additional number of back up patrols. Results identify a feasible level of ideal back up patrols in all evaluated police districts. Recommendations are provided to reconsider redistricting strategies to assist the patrol deployment strategy.
A heuristic approach to the police staff scheduling problem
1993
problem as an ILP was invaluable. His steadfast su~por; and / direction during the time of the author's study lS much appreciated. Thanks go to Dr. John W. Adams and Dr. Robert H. storer for their insights to the mathematical structure of the problem. I would like to thank Off icer Robert Haffner for assisting me in the constraint definition phase of the problem, and for constantly keeping me on my toes with my work by inquir ing about my progress during any time around the clock. Many thanks to my colleagues Laura Louise Lansing and Yesim Erke for their insights into the problem and their constant moral support. I thank Margaret •P. Church for sharing her knowledge with me about the psychological effects of scheduling. Last, but not least, I thank my parents for their continuous moral support.