A Sales Route Optimization Mobile Application Applying a Genetic Algorithm and the Google Maps Navigation System

2019 ◽  
pp. 517-527
Author(s):  
Cristian Zambrano-Vega ◽  
Génesis Acosta ◽  
Jasmin Loor ◽  
Byron Suárez ◽  
Carla Jaramillo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Navigation System ◽  
Mobile Application ◽  
Route Optimization ◽  
Google Maps

scholarly journals WeRoute: Route Optimization Web-Based System and Driver Mobile Application

2021 ◽  
Vol 8 (2) ◽  
pp. 78-87
Author(s):  
Ang Pei Ying ◽  
Justtina Anantha Jothi ◽  
Nursakirah ARM
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mobile Application ◽  
Route Optimization ◽  
Optimal Route ◽  
Routing Problem ◽  
Web Based ◽  
Cost Controls ◽  
Web Based System

This paper intends to conceptualise an optimisation solution for vehicle routing that can get the best routing result and release the most optimal route to the driver, namely WeRoute. The objectives of the paper are to manage the data efficiently, save time, reduce cost, enhance customer satisfaction, and decrease the emission of carbon. Moreover, this is also known as the vehicle routing problem, which deals with a range of variables, including drivers, stops, roads, and customers. The method, Genetic algorithm, was developed to improve the efficiency of generating feasible routes for a project. A team of drivers and several stops are needed to generate the solution of optimising the vehicle routing. It can be said that the more drivers or stops, the more complicated the problem becomes, such as cost controls and vehicle limitations. Thus, a route optimisation tool slowly becomes the key to ensuring the delivery business as efficiently as possible.

scholarly journals Online Analytics for Shrimp Farm Management to Control Water Quality Parameters and Growth Performance

2021 ◽  
Vol 13 (11) ◽  
pp. 5839
Author(s):  
Siriwan Kajornkasirat ◽  
Jareeporn Ruangsri ◽  
Charuwan Sumat ◽  
Pete Intaramontri
Keyword(s):  
Water Quality ◽  
Growth Performance ◽  
Mobile Application ◽  
Database Management System ◽  
Quality Data ◽  
Analysis Tool ◽  
Quality Factors ◽  
Google Maps ◽  
Water Quality Data ◽  
Shrimp Farm

An online analytic service system was designed as a web and a mobile application for shrimp farmers and shrimp farm managers to manage the growth performance of shrimp. The MySQL database management system was used to manage the shrimp data. The Apache Web Server was used for contacting the shrimp database, and the web content displays were implemented with PHP script, JavaScript, and HTML5. Additionally, the program was linked with Google Charts to display data in various graphs, such as bar graphs and scatter diagrams, and Google Maps API was used to display water quality factors that are related to shrimp growth as spatial data. To test the system, field survey data from a shrimp farm in southern Thailand were used. Growth performance of shrimp and water quality data were collected from 13 earthen ponds in southern peninsular Thailand, located in the Surat Thani, Krabi, Phuket, and Satun provinces. The results show that the system allowed administrators to manage shrimp and farm data from the field sites. Both mobile and web applications were accessed by the users to manage the water quality factors and shrimp data. The system also provided the data analysis tool required to select a parameter from a list box and shows the association between water quality factors and shrimp data with a scatter diagram. Furthermore, the system generated a report of shrimp growth for the different farms with a line graph overlay on Google Maps™ in the data entry suite via mobile application. Online analytics for the growth performance of shrimp as provided by this system could be useful as decision support tools for effective shrimp farming.

scholarly journals Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Sun ◽  
Wenjun Yi ◽  
Dandan Yuan ◽  
Jun Guan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Initial Alignment ◽  
Elman Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

The purpose of this paper is to present an in-flight initial alignment method for the guided projectiles, obtained after launching, and utilizing the characteristic of the inertial device of a strapdown inertial navigation system. This method uses an Elman neural network algorithm, optimized by genetic algorithm in the initial alignment calculation. The algorithm is discussed in details and applied to the initial alignment process of the proposed guided projectile. Simulation results show the advantages of the optimized Elman neural network algorithm for the initial alignment problem of the strapdown inertial navigation system. It can not only obtain the same high-precision alignment as the traditional Kalman filter but also improve the real-time performance of the system.

scholarly journals Wireless Sensor Network Route Optimization Based on Improved Ant Colony-Genetic

2015 ◽  
Vol 11 (9) ◽  
pp. 4 ◽  
Author(s):  
Wei Liu ◽  
Yongfeng Cui ◽  
Zhongyuan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Problem ◽  
Ant Colony ◽  
Route Optimization ◽  
Wireless Sensor ◽  
Multi Objective Optimization ◽  
Mutation Operation

The objective of this paper is focuses on route optimization, for a given wireless sensor network. We detail the significance of route optimization problem and the corresponding mathematical model. After analyzing the complex multi-objective optimization problem, Ant Colony Optimization (ACO) algorithm was introduced to search the best route. Inspired by Genetic Algorithm (GA), we embed two operations into ACO to refine it. First, every ant after achieving sink will be regarded as an individual such as that in GA. The crossover operation will be applied and then, the generated new ants will replace the weaker parents. Second, we designed a mutation operation for ants selecting next nodes to visit. Experimental results demonstrate that the proposed combination algorithm has significant enhancements than both GA and ACO. The lifetime of WSN can be extended and the coverage speed can be accelerated.

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