Vehicles Routing Arrangement for Prevention and Suppression Patrol: Process Model of Police Stations in Thailand

Coronavirus disease has affected to economic economics and society results in the, resulting in increasing crime problems increasing. Under circumstances with limited patrol officers, the prevention and suppression operations must be analyzed for vehicle routing for prevention and suppression patrols to reduce the patrol distance, time and fuel. This research aims to analyze the vehicle routing arrangement of the prevention and suppression patrol officers and to develop a routing process model of the prevention and suppression patrol management. Quantitative research is applied with Traveling Salesman Problem (TSP) the traveling salesman problem (TSP), which is utilized to established the establish prevention and suppression patrol routes. The qualitative Qualitative research is applied with content analysis, which is utilized to develop to the routing process model. The vehicle routes of the prevention and suppression patrol are arranged in the route set. These results are shown show the average of the route, distance and shortest patrol time of the patrol officers, which are cover all risk point patrol. Also, patrols. Additionally, a patrol routing model is presented to arrange the process of prevention and suppression patrol operation. This key process is applied to well manage the prevention and suppression patrol well, and these benefits are to reduce the patrol distance, patrol time and fuel cost. Additionally, this practice has increased the patrol rounds at risk points, resulting in the reduction in crimes and upgrading the good practices in patrol operations.


Introduction
Coronavirus disease 2019  has resulted in an outbreak around the world that has an effect on the world economy and society. Crime problems and disorders are increasing to society, and people need more security in life and property from the state. The Royal Thai Police is the main organization that is an important operation to protect the life and property of the people by preventing crime. This main goal of achieving mission policies is de ned as police station practice for reducing public crime and preventive and suppressive crime (Famega 2005).
Therefore, e ciency of prevention and suppression is inevitably needed. Equipment and knowledge of prevention and suppression management require the development of crime prevention and suppression practices. The police patrol (patrol o cers) has played an important role in achieving the primary duty of the police station. When crime prevention is achieved, this support increase is improved to the safety of society and life people in the whole system. Police patrols are a signi cant part of crime prevention and the quality that is necessary for prevention and suppression operations (Sherman et al. 2014). These data show that prevention and suppression work is developed to reduce crime management if there is a plan for good management and good prevention and suppression.
Reducing crime and being able to stop the incident, the main practice of prevention and suppression are worked in the patrol with patrol planning consisting of checkpoints or risk points. These arranged patrol routes are covered in areas responsible for police stations in various risk areas from the analysis of problems and obstacles in the operation of the patrol through a weekly meeting with the analysis of crime conditions. This nding found the main problem in the route of the patrol, which limited the patrol o cers, patrol time and cost.
These problems are solved transport and logistics management by the traveling salesman problem (TSP).
Patrol route analysis in the area is arranged by the e ciency distance, time and cost of patrol operation planning (Zia et al. 2018;Almahasneh & Koczy 2020). Therefore, these problems are interested in nding rst solutions to solve these complications and to improve the e ciency of prevention and suppression patrol routes. This research aims to analyze vehicle routing arrangements of prevention and suppression patrols and to develop a patrol process model. This information supports the e ciency of prevention and suppression patrols and increases the maintenance of life/property safety of people; additionally, potential crime problems must be reduced.

Prevention and suppression patrol concepts
Prevention and suppression crime refer to the various methods that are classi ed as eliminating the incident source, eliminating the desire to commit wrongdoing and eliminating the potential for wrongdoing. These main police duties are operated to break the crime scene in which arresting and controlling criminals are to avoid the crime of committing added crime and punishing criminals to cause disgust. Anti-crime measurement can be divided into two measures: 1.) Preventive measurement includes basic preventive procedures in which there are similarities and differences between these normal and proactive preventive measures. 2.) the measurement to suppress that are divided into normal suppression measurement and aggressive suppression.
In addition, crime prevention theory is classi ed into two groups: 1.) The theory reduces the likelihood of wrongdoing criminals, eliminating or reducing the likelihood of wrongdoing 2.) Informal social control theory is based on the assumption that crime is caused by community weakness and social control failure (Greenberg et al., 1985). Furthermore, modern crime prevention theory, "5 theories, 1 principle", is 1.) Law enforcement theory of catching criminals that crime prevention is the primary job of the police and more important than investigation. 2.) Police community relations theory is a friendly community that reduces con icts and increases communication channels between the police and the public 3.) Community policing police theory serves as service provider 4.) Crime control through environmental design theory 5.) Broken window theory as an inspector of order 6.) Principles of development and problem solving that uctuate according to community guidelines by acting as an analyst and problem solving (Herbert Gans 1962).

Traveling Salesman Problem (TSP)
In the concept solving of the route problem deciding, there is one starting point and multiple endpoints (travel to each point only once) by the route that this travel must portable through every point and nally return to the starting point. The objective is to analyze all travel points with the lowest distance. These data will affect the travel e ciency in terms of duration and fuel consumption and route management system. Thought is widely applied to solve problems in various elds, such as cargo routing, tourism routing, and travel planning. The TSP problem has been focused on the route of travel when there are N cities or places for needing to be traveled. The journey will travel from any one of N cities by route travel. Through all cities in N and return to the starting city to tread like a walking loop.
Various studies have applied this concept to develop the utilization of distance, time and cost. Tao's (2008) TSP problem solution based on an improved genetic algorithm states that the PMX crossover method can be applied to TSP. Its shortcomings are: 1.) The scope is strictly limited for PMX crossover methods, and PMX crossover methods cannot realize partial chromosomes. 2.) The PMX crossover method, where a 2-point crossover is not good for excellent chromosomal heredity. According to Su et al. (2009) New crossover operator of genetic algorithms for the TSP. With the new crossover method "cut-blend crossover" for the genetic algorithm for the salesperson travel problem, cut-blend crossover may be the best crossover. The proposal was inserted in the process of the New Genetic Algorithm in which well-known standard problems such as oliver30, eil76, ch130 and pcb442 were used in TSPLIB for experimentation. The new crossover is better than the old methods, such as OX and ER, and is especially suitable for the problem of large sizes. Yi et al. (2010) stated that the improved hybrid genetic algorithm for solving TSP based on Handel-C, the salesperson travel problem (TSP), is a common problem that can be easily explained. However, it is di cult to solve the problem with good performance, and this problem is widely applied in practice. Therefore, solving the salesperson journey (TSP) quickly and e ciently is very important in practice. The genetic algorithm is a method for nding answers by optimizing problem solving. In this research, Handel -C language was used to write a simple program and improve the genetic algorithm to solve the problem. The results showed that the algorithm's performance was greatly improved. According to Nian & Jinhua (2011). The hybrid genetic algorithm for TSP uses a simple genetic algorithm for solving salesperson travel problems. The best route uses very randomness and does not consider neighborhoods. To reduce randomness, this research proposes a hybrid genetic algorithm based on an algorithm that improves usability by obtaining data from previous versions. It will also increase the local search process to make it more useful, and information that will be sent to obtain the best answers.
Hassan (2012) developed an improved greedy crossover to solve the symmetric traveling salesman problem in which the travel salesman problem (TSP) is the most famous optimization problem. In this research, an improved version of the greedy crossover presented greedy crossover to conventional crossover in the past by comparing speed and accuracy. According to Sangwan et al. (2018), the traveling salesman problem (TSP) is a classical combinatorial optimization problem that is simple to state but very di cult to solve. The problem is to nd the shortest tour through a set of N vertices so that each vertex is visited exactly once. This problem is known to be NP-hard and cannot be solved exactly in polynomial time. Many exact and heuristic algorithms have been developed in the eld of operations research (OR) to solve this problem. In this paper, we provide an overview of different approaches used for solving the travelling salesman problem.

Research design
This research designed mix method research in two phases: the quantitative research approach of the rst phase to nd the route of prevention and suppression patrol arrangement to the route set. In the secondary phase, the prevention and suppression patrol process model is developed with a ow process chart as the framework of the research approach shown in gure 1.

Population and sample
Page 5/12 The population in this study is the data on distance patrol, time patrol, and fuel consumption of the area in responsibility of a police station in Chonburi Province, Thailand. There are a total of two responsible monitoring areas. In this research, nonprobability sampling was used purposive sampling with judgmental sampling methods that focused on the study of the main routes in red cabinet inspection (risk points). The inspection area is responsible for a case study, which has a total of twelve risk points.

Data analysis
The traveling salesman problem (TSP) is a tool for solving the problem of deciding the route of travel when there is one starting point and multiple endpoints (travel to each point only once) by the route. The travel must travel through every point and nally return to the starting point, as shown in gure 2. The objective is to analyze all travel points with the lowest distance. These data will affect the travel e ciency in terms of duration and fuel consumption and route management system (

Vehicles Routing Arrangement for the Prevention and Suppression Patrol
Police patrol o cers organized patrols into two turns of twelve hours each as the rst section (between 08.01 am. -8.00 pm.) and the second section (between 8.01 pm. -08.00 am.). The red checkpoints (risk checkpoints of patrol) in the responsible area show twelve checkpoints, which the police station to all twelve checkpoints of the responsible area using the numbers 0-12 as a symbol instead. Table 1 summarizes the preceding vehicle routing arrangements that present the average data of the route, distance and time of patrol vehicles of the district patrol. Patrol o cers have three sets for supporting the prevention and suppression patrol on o cial days (Monday-Friday) and holidays (Saturday-Sunday).
The results of vehicle patrol routes analyzed by the traveling salesman problem (TSP) using the nearest neighbor method (choosing to patrol to the city that is closest distance from the current checkpoint) are presented in Table 2. These are shown the distance routes between each checkpoint of the vehicle patrol route at all twelve checkpoints using Google Maps.
Starting from police station (0), which is the point where the vehicle patrol must round to inspect each responsibility area by choosing the shortest distance routes in the table and agreeing with the point in the row patrol to that main position with the shortest distance, the total distance must be calculated to be 29.7 kilometers. Therefore, these vehicle patrol results are utilized on o cial days (Monday-Friday), and holidays (Saturday-Sunday) are route 0-1-2-12-9-8-7-6-5-4-3-11-10 and back to 0, as shown in Table 3.
The results of vehicle patrol routes by TSP analysis are shown in vehicle patrol districts (routes 0-1-2-12-9-8-7-6-5-4-3-11-10 and back to 0) on o cial days (Monday-Friday) and holidays (Saturday-Sunday). The results showed that round of this district uses a total distance of 31.5 kilometers, a period of 78 minutes, and an amount of fuel of 1.819 liters. The results of the comparison of distances before and after vehicles patrol routes are arranged by analyzing TSP found that the average distance routes of before the patrol route arranged, the distance was 32.87 kilometers, time 139.58 minutes. After the patrol route was 31.5 kilometers, time 76 minutes on o cial days and the patrol route was 31.5 kilometers, time 74 minutes on holidays. The patrol routing distance was reduced by 1.37 kilometers, the patrol time was reduced by 63.58 minutes on o cial days and was reduced by 65.58 minutes on holidays.
After arranging vehicle patrol routes, there was a noticeable reduction in duration, and a number of factors led to the shortening of time, such as streamlined tra c conditions and red checkpoints after analyzing the TSP.
Additionally, e ciency improvement reduces the patrol time and distance; moreover, patrol o cers can increase the patrol cycle of vehicle patrols per route. Table 4 shows the results summary of the comparison of distances before and after the routing of the patrol on o cial days and holidays, which represent the e ciency improvement.

A process model of patrol routing arrangement for prevention and suppression
A ow process chat is applied to develop the process model of patrol routing arrangements that are preferred for process mapping for prevention and suppression routing. First, the structure of the inspection plan collects data by patrol route point information and distance from each point using Google Maps, although the information is not competing and needs survey data. This completed information to the secondary phase is analyzed with TSP using the Microsoft Excel program to obtain a patrol routing arrangement; however, the results are nonaccepted and need to check the database. In the third phase, patrol routing of prevention and suppression is designed in introduction practice and is analyzed criminally to improve the results. In the fourth phase, a patrol routing arrangement is tested in the introduction practice, and these results show high performance for improving the nal phase. Nevertheless, patrol route testing shows low performance and needs to be returned to the rst phase to improve the new task. In the nal phase, a patrol route practice plan is developed to operate the patrol routing of patrol policies for prevention and suppression management, as shown in Figure 3.

Conclusions
This research is focused on vehicle routing arrangements to improve the effectiveness of patrol routing by applying the traveling salesman problem (TSP) concept to patrol routing. The results of before and after on vehicles routing arrangement are represent the comparative results from applying the (TSP) to vehicles routing arrangement that are reduce the patrol time and distance effecting to reduce the fuel use. Police stations and patrol o cers have a limited number of patrol o cers and limited patrol time for prevention and suppression.
These results are the rst solution for improving management, which is the utilization of police use, time e ciency, and cost reduction in fuel use.
Additionally, the patrol cycle of vehicle patrols per route is increased after patrol routing on o cial days and holidays. The implementation practice is presented by a process model of patrol routing arrangement for the prevention and suppression that is applied to operate patrol o cers toward effectiveness management. Finally, Figure 1 Framework of qualitative research approach Process model of prevention and suppression patrol management