Journal of Robotics, Networking and Artificial Life
Vol. 8(3); December (2021), pp. 201–204
DOI: https://doi.org/10.2991/jrnal.k.210922.010; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

A Design and Development of WebGIS Distribution
System based on Smart Logistics
Hongbo Hao1, Fengzhi Dai1,2,*
College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China
Department of Design, Tianjin Tianke Intelligent and Manufacture Technology Co., Ltd, Tianjin, China

12ARTICLE INFOABSTRACTArticle History

The vigorous development of the logistics industry provides a solid foundation for China’s economic prosperity. However, withthe concept of “smart logistics” proposed, the transformation of the logistics industry to information technology presents greatchallenges. Aiming at the transportation problem in logistics industry, based on the analysis of the research status at home andabroad, this paper designs a balanced/unbalanced transportation problem solving method based on Monte Carlo similarity andgenetic algorithm. In this paper, Monte Carlo similarity method is introduced to design and develop a WebGIS transportationand distribution system based on genetic algorithm. This paper analyzes the information requirements, functional requirements
and performance requirements of the transportation and distribution system and gives the overall architecture of the system. Thesystem designs the database table relations and table in detail. Finally, the running test results show that the system can effectively
reduce the distribution cost, increase enterprise profits and improve enterprise efficiency.

Received 25 October 2020
Accepted 25 July 2021
Keywords
Smart logistics
balanced/unbalanced
transportation problem
WebGIS© 2021 The Authors. Published by Atlantis Press International B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Modern logistics industry is based on information technology andtransportation technology, so the logistics process itself is completed by the transportation system supported by informationtechnology. If modern logistics industry is not supported by transportation industry, it is not the real logistics industry. The modernlogistics industry is composed of goods packaging, goods loading
and unloading, goods allocation, goods management and goods
storage and transportation, which are all around transportation.
Therefore, the study of transportation is very important and it has
a strong practical significance.
In this paper, a genetic algorithm based on Monte Carlo similarity
is proposed for transportation problem, and Monte Carlo similarity operator is constructed to avoid the algorithm falling into local
optimal solution. The research and development system has a certain practical significance for the promotion and development ofintelligent logistics [1].
An intelligent optimization algorithm based on the combinationof Monte Carlo similarity and genetic algorithm is designed. The
Monte Carlo similarity receiving method is introduced, and theconclusion that the genetic algorithm based on Monte Carlo andadopting matrix coding is better [2].
This paper introduces the transportation problems in logistics
industry and the research background and the significance ofWeb Geographic Information Systems (WebGIS). It expounds theCorresponding author. Email: daifz@tust.edu.cn*

transportation problems and the research status of WebGIS athome and abroad. It is combined with Arc Geographic Information
Systems (ArcGIS) platform. It is studied and designed the transportation problem solving method combining Monte Carlo similarityand genetic algorithm, and based on this theory. It develops thetransportation problem solving method including task list module,task creation module, task management module, task management
module and task management module. The WebGIS transportationand distribution system consist of six functional modules: OriginalDuration (OD) cost module, distribution scheme module, task analysis module and personal center module.

2. OVERALL SCHEME DESIGNThis paper analyzes the research background and significance oftransportation problems in logistics industry. And it introduces theresearch status of transportation problems and WebGIS technology at home and abroad. This paper describes some basic theories
involved in the algorithm, such as genetic algorithm, Monte Carlo,
similarity theory and related technical framework of WebGIS
transportation and distribution system.

2.1. Front End ArchitectureThis paper uses bootstrap that is very popular nowadays. Vue.js.
Bootstrap is an open source CSS framework developed by twitterengineers Jacob Thornton and Mark Otto to improve code reusability. Bootstrap is a flexible, elegant, easy to use and extensible202

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front-end framework.Vue. It is a progressive framework for building the front-end interface. Different from other frameworks, it canbe applied layer-by-layer from bottom to top and developed basedon Model-View-ViewModel (MVVM) mode Vue.JS Framework[3]. MVVM pattern is Vue.JS, one of the biggest features of the pattern architecture that is shown in Figure 1.

and the sales volume is Qj. The cost of transporting a unit of goods
from Wi to Qj with a certain route s in a certain period of time isCij(t, s), which is represented by Cij below. Schematic diagram oforigin and sale of place [4] is shown in Figure 3.

2.2. Back End Architecture

The fitness function of genetic algorithm is used as an index toevaluate the quality of individuals in a population. The larger thefitness value is, the better the individual is. The fitness function isshown below.

The Springboot solves the characteristics of various configuration,
low development efficiency and complex deployment of back-end
development. The Maven dependency is simplified by using start
Page Object Model (POM), and the configuration is completedby annotation. The single instance servlet improves the runningefficiency. It is adopted, combined with spring, spring Model View
Controller (MVC) and mybatis framework to complete the development. Therefore, spring takes Intelligent Operations Center(IOC) and Aspect Oriented Programming (AOP) design patterns
as the core to construct a bean container to store singleton objects.
The architecture of spring is shown in Figure 2.

3. DESIGN AND SOLUTION OF
TRANSPORTATION MODEL3.1. Balanced Transportation ModelThe equilibrium transportation problem is described as follows:
suppose a product has m producing areas W1, W2, ..., Wm and Noutlets Q1, Q2, ..., Qn. Among the output of producing area is Wi,

3.2. Fitness Function Design		 f ( X ) = e− l tr (CT X ) −w (1)

3.3. Solving Transportation Problems
The algorithm process steps are as follows:•• Set the operation parameters;•• Initial population;•• The fitness of each chromosome was calculated;•• The chromosomes were sorted according to the fitness;•• The first 10% individuals with high fitness were reserved;•• Monte Carlo similarity method is used to receive the difference solution between the mutated individual and the mutatedparent [5].

3.4. Algorithm Results and Analysis
Transportation problem is a very common research topic in real life.
In the actual transportation problem, it is often necessary to consider the factors such as comprehensive scheduling, task arrangement and weather environment. Its essence is a complex schedulingproblem, which is classified into the category of linear programming and solves a kind of problem of the dispatching scheme of
production and sales places. Transportation problems are mainly
divided into two categories. One is balanced transportation problem, the other is unbalanced transportation problem.
Figure 1 | Vue.JS MVVM mode architecture diagram.

In this chapter, firstly the models of balanced and unbalanced transportation problems are described, and the transformation relationship between balanced and unbalanced transportation problems isFigure 2 | Spring architecture diagram.

Figure 3 | Schematic diagram of origin and sale of place.

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discussed. Then the Genetic Algorithm (GA) based on PR ü fer
number coding and the GA algorithm based on matrix coding are
introduced to solve the balanced transportation problem [6].
In view of the above-mentioned algorithm, the Monte Carlo similarity receiving method is designed, and the dynamic mutation
rate and random mutation strategy are adopted. Finally, the averagevalue of the optimal value in each iteration, the fitness of the average value of the optimal value in each iteration and the deviation
rate between the average value of the optimal solution. The known
optimal solution under different scales of Traditional Genetic
Algorithm (TGA), Micro-Genetic Algorithm (MGA), Improved
Traditional Genetic Algorithm (ITGA) and Immune GeneticAlgorithm (IMGA) are compared. The results show that the performance of IMGA algorithm is better than the other three algorithms.
TGA, MGA, ITGA and IMGA algorithms converge to the averagefitness of the optimal value of each iteration are shown in Figure 4.

4. DESIGN OF WebGIS
DISTRIBUTION SYSTEM
4.1. System Requirements Analysis
The WebGIS transportation and distribution system abandons theprevious purely digital representation. It combines the GIS system
to introduce spatial relationships into the transportation and distribution system, and selects production and sales locations in an
intuitive and optional way. The system is mainly based on the task
creation module. It is supplemented by task list module, OD costmodule, distribution plan module, task analysis module and personal center module.

4.2. System Technical Route
The WebGIS transportation and distribution system adopts theB/S architecture model, which reduces the burden of the client on
the server and the cost of system maintenance and upgrades. Thesystem server level uses the GIS server in ArcGIS Enterprise and theFigure 4 | TGA, MGA, ITGA and IMGA algorithms converge to theaverage fitness of the optimal value of each iteration.

203

Tomcat-based Web server. The system uses the Spring SpringMVCMyBatis (SSM) framework, which is divided into a presentation
layer, a business processing layer and a data persistence layer [7].

4.3. System Database Design
The design of the system is mainly based on the task creation
module. The table structure and relationship are designed in atask-driven way, including system user table, task list, expensetable, expense detail table, distribution table, distribution details
table, and algorithm analysis table, sales surface, algorithm iteration record table, etc.

4.4. System Function Module Design
According to the actual system requirements, the WebGIS transportation and distribution system is equipped with various module
functions of WebGIS, which are mainly divided into six modules:
task list module, task creation module, OD cost module, distribution plan module, task analysis module and personal center module.

4.5. System Function Module Analysis
The distribution plan module plays the role of query of calculation
results in the WebGIS transportation and distribution system. Thismodule undertakes the task creation module and the OD expensemodule, obtains the preprocessing data necessary for the algorithm.
It calls the intelligent algorithm according to the task. The distribution plan module visually allows users to view the distribution ofproduction and sales. With WebGIS technology, the production andsale area are marked in the form of a map. The number and shapes
are combined to achieve the purpose of abstract digital visualization.

5. CONCLUSION
This article aims at the completeness and ease of use. The specificoperation process of the WebGIS transportation and distribution
system is shown in Figure 5.

Figure 5 | WebGIS distribution system flow chart.

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This paper takes the transportation and distribution system of alogistics company as the background. It combines the Monte Carlosimilarity GA transportation problem algorithm based on matrix
coding on the basis of considering the actual transportation problem requirements. The front-end framework uses Bootstrap, Vue.jsand ArcGIS API for JavaScript [7]. The back-end frameworkadopts SSM and other Web system development technologies, andwith the help of ArcGIS platform.
The WebGIS system that meets transportation and distribution is
finally designed and realized. The system uses Intellij idea as theintegrated development environment. The core algorithms andback-end services are built in Java language. The front-end development framework uses ArcGIS API for JavaScript, Bootstrap andVue.js, etc., and data storage and management uses MySQL andArcGIS DataStore [8].

CONFLICTS OF INTERESTREFERENCES
[1]

[2]
[3]
[4]
[5]
[6]

The authors declare they have no conflicts of interest.

ACKNOWLEDGMENTS
The research is partly supported by the Project of Tianjin
Enterprise Science and Technology Commissioner to Tianjin
Tianke Intelligent and Manufacture Technology Co., Ltd(19JCTPJC53700). It is also supported by the Industry-University
Cooperation and Education Project (201802286009) from
Ministry of Education, China.

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AUTHORS INTRODUCTION
Ms. Hongbo HaoDr. Fengzhi Dai
She is a first-year master candidate in Tianjin
University of Science and Technology, majoring in pattern recognition, principle of automatic control, engineering mathematics andother important control disciplines. Her
research area is about deep learning andimage processing. During her study, she has
published several research papers.

He received an M.E. and Doctor ofEngineering (PhD) from the Beijing Instituteof Technology, China in 1998 and Oita
University, Japan in 2004 respectively. His
main research interests are artificial intelligence, pattern recognition and robotics. He
worked in National Institute of Technology,
Matsue College, Japan from 2003 to 2009.
Since October 2009, he has been the staff in Tianjin Universityof Science and Technology, China, where he is currently an
Associate Professor of the College of Electronic Information
and Automation.