Publication: Estudio de técnicas de planificación dinámica de planes de ocio
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2017
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Abstract
Este proyecto, 'Olétrip', es la continuación de una versión anterior cuyos autores fueron Raquel Álvarez Hernández, Qiang Sun y YanYan Cheng.
Nuestro objetivo en el proyecto es mejorar las recomendaciones de un sistema de planes de ocio usando diferentes algoritmos para calcular las rutas más óptimas con la ayuda de un validador personalizado.
Los algoritmos nuevos implementados en la aplicación son los siguientes:
• Algoritmo aleatorio: se implementó para ayudarnos a familiarizarnos con el código heredado del proyecto anterior, y posteriormente resultó útil para la implementación del algoritmo genético.
• Algoritmo de primero el mejor (BFS): se usa para la búsqueda de la ruta más corta después de que el usuario haya especificado todos los detalles del viaje.
• Algoritmo genético: este algoritmo se realizó con la idea de construir un plan óptimo, a pesar de saber que requiere de bastante tiempo y consumo de recursos... pero finalmente se optó por ese algoritmo para los viajeros que saben a qué ciudad quieren ir pero que no saben qué visitar.
Se explicará las ventajas y desventajas que nos supone optar por un algoritmo u otro, el cómo se ha implementado cada uno de ellos y los cambios que han sido necesarios abordar con respecto a la anterior versión del proyecto.
Incorporaremos a Olétrip un validador de planes que se encargará en la parte del backend de considerar la eficiencia que tiene un plan partiendo de unos requisitos que hemos definido.
Será de gran importancia esta nueva funcionalidad de validación ya que los algoritmos implementados manejan una cantidad importante de planes y de esta manera nos ayuda a evaluar qué planes son más eficientes.
La interfaz utilizada será la misma que en su anterior versión, incorporando una interfaz nueva que sirve para el recomendar de planes a los usuarios indecisos.
También hemos incorporado un simulador de planes para los usuarios Admin que ayuda a la hora de implementar alguna funcionalidad nueva en la aplicación.
De esta manera realizaremos una serie de evaluaciones con el usuario realizando un estudio de los distintos algoritmos, analizando así los resultados obtenidos y la satisfacción del usuario.
‘Oletrip’ is a touristic App that was created las year by the students Raquel Álvarez Hernández, Quiang Sun y YanYan Cheng as a final project. This year our teams object is to improve this App by making better recommendations of a leisure plans system, using different type of algorithms to calculate the best road for a customer plan, with the help of a validator that was personalized by us. The new algorithms that we implemented in our app are: • Random algorithm: it was our first implementation in the app, it helped us as an introduction to the app developing because as we know we took a started app. Then it had it benefits because we needed a random algorithm for the implementation of the Genetic algorithm. • Best First Search: we use it for the searching of the shortest route after the user specify all the details of the trip. • Genetic algorithm: this algorithm was made with the idea of making the best plan, the inconvenient of using this algorithm is that it takes a lot of time building the best trip and consumes many resources at the same time. But we finally had the idea to use it to recommend people that don’t know what to visit in the cities that they chose. We will explain the advantages and disadvantages of using every algorithm, how it was implemented and the changes that we needed to do to integrate our code to the last year’s version App. Olétrip now has a validator that was implemented to help us to know what plan is better, for this validator we defined some requirements that we considered as a team to value the plans. This functionality is so important for the new algorithm’s that we developed because of the many plans that these algorithms generates. The app design will be the same except for some new incorporations that we developed that are: A new interface that contains a planning recommendation for users that don’t know what to visit in the cities that they will travel to. A developer option that helps to simulate a trip, for this option you need to have an admin role and it helps for the implementation of a code when you need an example to see if the code works. Finally, we will contact some users to try our app and tell us what planes they like more depending of the algorithm that we use including the algorithms that were developed in the previous version, and we will analyze the results
‘Oletrip’ is a touristic App that was created las year by the students Raquel Álvarez Hernández, Quiang Sun y YanYan Cheng as a final project. This year our teams object is to improve this App by making better recommendations of a leisure plans system, using different type of algorithms to calculate the best road for a customer plan, with the help of a validator that was personalized by us. The new algorithms that we implemented in our app are: • Random algorithm: it was our first implementation in the app, it helped us as an introduction to the app developing because as we know we took a started app. Then it had it benefits because we needed a random algorithm for the implementation of the Genetic algorithm. • Best First Search: we use it for the searching of the shortest route after the user specify all the details of the trip. • Genetic algorithm: this algorithm was made with the idea of making the best plan, the inconvenient of using this algorithm is that it takes a lot of time building the best trip and consumes many resources at the same time. But we finally had the idea to use it to recommend people that don’t know what to visit in the cities that they chose. We will explain the advantages and disadvantages of using every algorithm, how it was implemented and the changes that we needed to do to integrate our code to the last year’s version App. Olétrip now has a validator that was implemented to help us to know what plan is better, for this validator we defined some requirements that we considered as a team to value the plans. This functionality is so important for the new algorithm’s that we developed because of the many plans that these algorithms generates. The app design will be the same except for some new incorporations that we developed that are: A new interface that contains a planning recommendation for users that don’t know what to visit in the cities that they will travel to. A developer option that helps to simulate a trip, for this option you need to have an admin role and it helps for the implementation of a code when you need an example to see if the code works. Finally, we will contact some users to try our app and tell us what planes they like more depending of the algorithm that we use including the algorithms that were developed in the previous version, and we will analyze the results
Description
Trabajo de Fin de Grado en Ingeniería Informática (Universidad Complutense, Facultad de Informática, curso 2016/2017)