Técnicas Inteligentes en la gestión de la industria turística

• Autores: Anita Krupskaia Herrera Vaca
• Directores de la Tesis: Ángel Arroyo Puente (dir. tes.) , Alfredo Jiménez Palmero (dir. tes.)
• Lectura: En la Universidad de Burgos ( España ) en 2024
• Idioma: español
• Número de páginas: 100
• Enlaces
  • Tesis en acceso abierto en: RIUBU
• Resumen
  • español

    La industria turística representa una oportunidad en el desarrollo de diferentes localidades, gracias a las inversiones realizadas en infraestructura y servicios, así como a la generación de empleo, factores que impulsan el crecimiento económico y social. Esta industria ha experimentado transformaciones rápidas y profundas, alcanzando mayor eficiencia en la gestión de los recursos, optimizando la planificación y mejorando la operación de los servicios turísticos, todo esto principalmente impulsado por la adopción de nuevas tecnologías. En este contexto, las técnicas de Machine Learning (ML) se muestran como un recurso prometedor frente a una industria que debe innovar de acuerdo a los requerimientos de los turistas. La integración de ML, permite analizar grandes conjuntos de datos para adaptarse a las demandas cambiantes del mercado y ofrecer servicios más eficientes, impulsando así la innovación y la competitividad de la industria turística. La presente tesis doctoral aborda el estudio de las técnicas de ML en el ámbito de la gestión turística, tratado en tres artículos de investigación que han sido aprobados para la publicación en revistas científicas indexadas en JCR. 1. El primer artículo se centra en la revisión y síntesis de investigaciones previamente publicadas, sobre la Inteligencia Artificial en el sector turístico. El estudio presenta una categorización de las aplicaciones de Inteligencia Artificial en diferentes áreas del turismo, reconociendo estudios y herramientas válidas para el crecimiento e innovación del sector y destacando la apropiación de la Inteligencia Artificial por parte de la industria turística 2. El siguiente estudio utiliza técnicas de Soft Computing para analizar variables relacionadas con la operación de las empresas turísticas de Ecuador, verificando la tendencia de la operación en diferentes años y generando una fuente de información válida para la toma de decisiones. En el estudio se aplican técnicas de reducción de dimensionalidad con el objeto de mejorar la interpretación minimizando la pérdida de información. Además, se aplican técnicas de agrupación para crear grupos acorde a la similaridad de las características y proporcionar una representación visual y numérica de la relación de los datos entre sí. 3. El tercer artículo se enfoca en el uso de las técnicas de ML para prever cancelaciones de reservaciones de hotel. El trabajo analiza e implementa pasos clave como el preprocesamiento de datos, la configuración de hiperparámetros y la evaluación de los modelos utilizando métricas y gráficas de rendimiento. El artículo incluye clasificadores base, clasificadores de conjunto y redes neuronales. En los estudios analizados en esta tesis, se destaca la eficacia de las técnicas de ML en la generación de información valiosa para respaldar la toma de decisiones en la gestión turística. Al analizar las variables relacionadas con la operación de las empresas turísticas, es posible identificar la tendencia de la operación en diferentes períodos de tiempo, reconociendo además el efecto de factores externos. Así también, a partir de las técnicas de ML es factible obtener modelos de pronóstico con alta precisión, muy útiles en la gestión para anticipar tendencias y optimizar la planificación en el sector turístico. Asimismo, una exhaustiva revisión de la literatura relacionada con la Inteligencia Artificial en la industria turística a través de aplicaciones, evidencia cómo estas tecnologías transforman la manera de ofrecer servicios, a la vez que enriquecen la experiencia del usuario, impulsando la innovación y desarrollo en el sector. En síntesis, estas técnicas son un recurso de gran ayuda, que permiten alcanzar mejores niveles de competitividad en un mercado en constante evolución.

    The tourism industry represents an opportunity for the development of different localities, thanks to the investments made in infrastructure and services, as well as the generation of employment, factors that drive economic and social growth. This industry has undergone rapid and profound transformations, achieving greater efficiency in the management of resources, optimizing planning and improving the operation of tourism services, all of this driven mainly by the adoption of new technologies. In this context, Machine Learning (ML) techniques are a promising resource for an industry that must innovate according to tourists' requirements. The integration of ML allows analyzing large datasets to adapt to changing market demands and offer more efficient services, thus boosting innovation and competitiveness of the tourism industry. This doctoral thesis addresses the study of ML techniques in the field of tourism management, addressed in three research articles that have been approved for publication in scientific journals indexed in Journal Citation Reports. 1. In the first article, Soft Computing techniques are used to analyze variables related to the operation of tourism companies in Ecuador, verifying the trend of the operation in different years and generating a valid source of information for decision making. In the study, dimensionality reduction techniques are applied to improve the interpretation, minimizing the loss of information. In addition, clustering techniques are applied to create groups according to the similarity of the characteristics and to provide a visual and numerical representation of the relationship of the data with each other. 2. The following study focuses on the review and synthesis of previously published research on Artificial Intelligence in the tourism sector. The study presents a categorization of the applications of Artificial Intelligence in different areas of tourism, recognizing valid studies and tools for the growth and innovation of the sector and highlighting the appropriation of Artificial Intelligence by the tourism industry. 3. The third paper focuses on the use of ML techniques to foresee hotel reservation cancellations. It discusses and implements key steps such as data preprocessing, hyperparameter settings, and model evaluation using performance metrics and graphs. The paper includes base classifiers, ensemble classifiers and neural networks. The studies analyzed in this thesis demonstrate the effectiveness of ML techniques to generate valuable information to support decision-making in tourism management. By analyzing the variables related to the operation of tourism companies, it is possible to identify the trend of the operation in different periods of time, also recognizing the effect of external factors. Also, from ML techniques it is possible to obtain highly accurate forecasting models, which are very useful in management to anticipate trends and optimize planning in the tourism sector. Likewise, an exhaustive review of the literature related to Artificial Intelligence in the tourism industry through applications, shows how these technologies transform the way of offering services, while enriching the user's experience, driving innovation and development in the sector. In short, these techniques are a very helpful resource to improve competitiveness levels in a constantly evolving market.

  • English

    The tourism industry represents an opportunity for the development of different localities, thanks to the investments made in infrastructure and services, as well as the generation of employment, factors that drive economic and social growth. This industry has undergone rapid and profound transformations, achieving greater efficiency in the management of resources, optimizing planning and improving the operation of tourism services, all of this driven mainly by the adoption of new technologies. In this context, Machine Learning (ML) techniques are a promising resource for an industry that must innovate according to tourists' requirements. The integration of ML allows analyzing large datasets to adapt to changing market demands and offer more efficient services, thus boosting innovation and competitiveness of the tourism industry. This doctoral thesis addresses the study of ML techniques in the field of tourism management, addressed in three research articles that have been approved for publication in scientific journals indexed in Journal Citation Reports.

    1.

    In the first article, Soft Computing techniques are used to analyze variables related to the operation of tourism companies in Ecuador, verifying the trend of the operation in different years and generating a valid source of information for decision making. In the study, dimensionality reduction techniques are applied to improve the interpretation, minimizing the loss of information. In addition, clustering techniques are applied to create groups according to the similarity of the characteristics and to provide a visual and numerical representation of the relationship of the data with each other.

    2.

    The following study focuses on the review and synthesis of previously published research on Artificial Intelligence in the tourism sector. The study presents a categorization of the applications of Artificial Intelligence in different areas of tourism, recognizing valid studies and tools for the growth and innovation of the sector and highlighting the appropriation of Artificial Intelligence by the tourism industry.

    3.

    The third paper focuses on the use of ML techniques to foresee hotel reservation cancellations. It discusses and implements key steps such as data preprocessing, hyperparameter settings, and model evaluation using performance metrics and graphs. The paper includes base classifiers, ensemble classifiers and neural networks.

    The studies analyzed in this thesis demonstrate the effectiveness of ML techniques to generate valuable information to support decision-making in tourism management. By analyzing the variables related to the operation of tourism companies, it is possible to identify the trend of the operation in different periods of time, also recognizing the effect of external factors. Also, from ML techniques it is possible to obtain highly accurate forecasting models, which are very useful in management to anticipate trends and optimize planning in the tourism sector. Likewise, an exhaustive review of the literature related to Artificial Intelligence in the tourism industry through applications, shows how these technologies transform the way of offering services, while enriching the user's experience, driving innovation and development in the sector. In short, these techniques are a very helpful resource to improve competitiveness levels in a constantly evolving market