scholarly journals Sistema informático para la distribución de uniforme escolar. Caso de estudio: provincia de Granma, Cuba

2021 ◽  
Vol 9 (19) ◽  
pp. 1-15
Author(s):  
Yamira Medel Viltres ◽  
Fidel Enrique Castro Dieguez ◽  
Angel Enrique Figueredo León ◽  
Alberto Rubén Leyva Polo ◽  
Adrián Almaguel Guerra
Keyword(s):  
Computer System ◽  
Flow Shop ◽  
Learning Algorithm ◽  
Real Data ◽  
Extreme Programming ◽  
School Uniforms ◽  
Q Learning ◽  
Model View Controller ◽  
Reinforced Learning ◽  
Comparison Of The Results

The distribution of school uniforms in the province of Granma is a process that is carried out in the different study centers of the province. The current research paper is aimed at developing a computer system that allows the control of the distribution of school uniforms in the province of Granma. It proposes a new algorithm based on Q-Learning to optimize the scheduling of the process of making school uniforms in clothing workshops. The Q-Learning algorithm of Reinforced Learning is a solution to the problem of sequencing tasks with a Flow Shop environment in a real context. The development of the computer system is based on free and multiplatform technologies. The technologies are HTML 5, CSS 3, JavaScript, Bootstrap, jQuery and CodeIgniter. Extreme Programming was used as an agile methodology of development of software and the Model-View-Controller as an architectural pattern. A comparison of the results obtained from the execution of the algorithm with real data of the entity is performed. After analysis of the tests carried out, usefulness and reliability of the software developed are checked, which contributes to the improvement of the distribution of school uniforms in the province of Granma.

scholarly journals Ramp Metering Control Based on the Q-Learning Algorithm

2015 ◽  
Vol 15 (5) ◽  
pp. 88-97 ◽  
Author(s):  
Edouard Ivanjko ◽  
Daniela Koltovska Nečoska ◽  
Martin Gregurić ◽  
Miroslav Vujić ◽  
Goran Jurković ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Ramp Metering ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Traffic Demand ◽  
Q Learning ◽  
Intelligent Transport ◽  
Reinforced Learning ◽  
Self Learning ◽  
Daily Changes

Abstract Modern urban highways are under the influence of increased traffic demand and cannot fulfill the desired level of service anymore. In most of the cases there is no space available for any infrastructure building. Solutions from the domain of intelligent transport systems are used, such as ramp metering. To cope with the significant daily changes of the traffic demand, various approaches with autonomic properties like self-learning are applied for ramp metering. One of these approaches is reinforced learning. In this paper the Q-Learning algorithm is applied to learn the local ramp metering control law in a simulation environment, implemented in a VISSIM microscopic simulator. The approach proposed is tested in simulations with emphasis on the mainstream speed and travel time, using a typical on-ramp configuration.

Application of improved Q learning algorithm to job shop problem

2009 ◽  
Vol 28 (12) ◽  
pp. 3268-3270
Author(s):  
Chao WANG ◽  
Jing GUO ◽  
Zhen-qiang BAO
Keyword(s):  
Job Shop ◽  
Learning Algorithm ◽  
Q Learning

Application of a Rough Set-Based Inductive Learning System

1993 ◽  
Vol 18 (2-4) ◽  
pp. 209-220
Author(s):  
Michael Hadjimichael ◽  
Anita Wasilewska
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Presidential Election ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Real Data ◽  
Semantic Content ◽  
Learning System ◽  
Voter Preferences

We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

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