Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2022
Abstract
Police patrol aims to fulfill two main objectives namely to project presence and to respond to incidents in a timely manner. Incidents happen dynamically and can disrupt the initially-planned patrol schedules. The key decisions to be made will be which patrol agent to be dispatched to respond to an incident and subsequently how to adapt the patrol schedules in response to such dynamically-occurring incidents whilst still fulfilling both objectives; which sometimes can be conflicting. In this paper, we define this real-world problem as a Dynamic Bi-Objective Police Patrol Dispatching and Rescheduling Problem and propose a solution approach that combines Deep Reinforcement Learning (specifically neural networks-based Temporal-Difference learning with experience replay) to approximate the value function and a rescheduling heuristic based on ejection chains to learn both dispatching and rescheduling policies jointly. To address the dual objectives, we propose a reward function that implicitly tries to maximize the rate of successfully responding to an incident within a response time target while minimizing the reduction in patrol presence without the need to explicitly set predetermined weights for each objective. The proposed approach is able to compute both dispatching and rescheduling decisions almost instantaneously. Our work serves as the first work in the literature that takes into account these dual patrol objectives and real-world operational consideration where incident response may disrupt existing patrol schedules.
Keywords
Dynamic Vehicle Routing Problem, Reinforcement Learning, Police Patrol, Scheduling, Bi-Objective
Discipline
Artificial Intelligence and Robotics | Software Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, Virtual Conference, 2022 June 13-24
Volume
32
First Page
453
Last Page
461
ISBN
9781577358749
Identifier
10.1609/icaps.v32i1.19831
Publisher
AAAI
City or Country
Singapore
Citation
JOE, Waldy; LAU, Hoong Chuin; and PAN, Jonathan.
Reinforcement learning approach to solve dynamic bi-objective police patrol dispatching and rescheduling problem. (2022). Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, Virtual Conference, 2022 June 13-24. 32, 453-461.
Available at: https://ink.library.smu.edu.sg/sis_research/7739
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/icaps.v32i1.19831