A literature review on police patrolling problems

Samanta, Sukanya; Sen, Goutam; Ghosh, Soumya K.

doi:10.1007/s10479-021-04167-0

Cited by 14 publications

(6 citation statements)

References 102 publications

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“…Car patrolling problems has been intensively studied in the literature, sometimes under different names because they can model different applications. Very recently, [16,17] surveyed police patrolling problems, by dividing them into three categories: (i) resource allocation, (ii) district design and (iii) route design. The route design category is the one that most resembles our problem because it is concerned with how the routes are selected and how they affect the patrol efficiency.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

Vidigal Corrêa,

Dong,

Iori

et al. 2023

Networks

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This paper addresses a real‐world multi‐period orienteering problem arising in a large Italian company that needs to patrol an area in order to provide security services to a set of customers. Each customer requires different services on a weekly basis. Some services are mandatory, while others are optional. It might be impossible to perform all optional services, and each of them is assigned a score when performed. The challenge is to determine a set of routes, one per day, that maximizes a weighted sum of the total collected score and total working time, while meeting several operational constraints, including hard time windows, maximum riding time, minimum number of services performed, and minimum time between two consecutive visits for the same service at the same customer. To solve the problem, we propose an iterated local search that invokes at each iteration an inner variable neighborhood descent procedure. Computational tests performed on a large number of real‐world instances prove that the developed algorithm is very efficient, and finds in a short time solutions that are consistently better than those produced by a mathematical model, and those in use at the company.

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Section: Brief Literature Reviewmentioning

confidence: 99%

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

Vidigal Corrêa,

Dong,

Iori

et al. 2023

Networks

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“…The DPRP belongs to a larger class of problem called police patrol problem. There are many aspects within the scope of police patrol problem ranging from designing of patrol district, resource allocation within a district, route design to combinations of any of these elements (Samanta, Sen, and Ghosh 2021). The discussion in this paper will mainly be on the routing and scheduling aspects of police patrol problem.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Approach to Solve Dynamic Bi-objective Police Patrol Dispatching and Rescheduling Problem

Joe

Lau

Pan

2022

ICAPS

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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.

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“…The patrol districting problem is widely used. It mainly consists of police patrol districting, emergency vehicle patrol districting and maritime patrol districting problems [13,14,21,22]. It uses the urban road network or census streets as a whole to achieve some of the goals of the patrol objectives, such as maximum coverage and workload standards [23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

Jiang

Zhao

et al. 2022

Complex Intell. Syst.

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Reasonable districting plays an important role in the patrolling process. In this paper, workload attributes are considered, and a mixed integer programming model is developed to solve the street patrol districting problem (SPDP). The improved spectral clustering algorithm named spectral clustering algorithm based on the road network (SCRn) and simulated annealing algorithm (SA) are combined. This results in a hybrid algorithm called SCRn-SA. The SCRn-SA algorithm is tested on small examples and real instances in Zhengzhou, China. The experimental results show that the proposed algorithm is effective for solving SPDP. It has better performance when compared to other advanced algorithms.

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A literature review on police patrolling problems

Cited by 14 publications

References 102 publications

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

Reinforcement Learning Approach to Solve Dynamic Bi-objective Police Patrol Dispatching and Rescheduling Problem

A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

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