Doctorado
Permanent URI for this communityhttps://hdl.handle.net/11285/551013
Colección de Tesis presentadas por alumnos para obtener un Doctorado del Tecnológico de Monterrey.
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- Artificial Intelligence Systems in Retail: Examining Customer Behavior and Adoption(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-20) Calvo Castro, Ana Valeria; Franco Valdez, Ana Dolores; emimmayorquin; Frasquet Deltoro, Marta; Amorós Espinosa, José Ernesto; EGADE Business School; Campus Ciudad de México; Valdez Cervantes, AlfonsoArtificial Intelligence (AI) is a disruptive innovation that has driven digital transformation in the retail industry. Technologies such as robots, chatbots, conversational agents, and generative AI are reshaping customer interactions. Although AI origins date back to the 1950s, when Alan Turing posed the question, “Can machines think?”, the use of this technology has exponentially evolved in recent years across various contexts and functions. These advancements increasingly simulate the capabilities of the human mind, enabling companies to achieve what once was considered impossible. This dissertation explores the antecedents and possible outcomes of AI technology within the retail industry, focusing specifically on AI acceptance and customers’ behavior regarding AI usage. The research undertakes an exploration of the role that AI systems could play in configuring and enhancing customer experiences through three interrelated articles, each offering unique insights into the role of AI in retail strategies. The first study provides a comprehensive analysis of insights on the impact of AI on omnichannel customer experience (OCE), incorporating perspectives from top-retail managers, consultants, and customers. The second study presents a conceptualization and validation of a measurement model for customers’ acceptance of artificial intelligence (CAAI), providing a robust framework for measuring AI acceptance among customers. The third, and final study, investigates and analyze the influence of CAAI on word-of-mouth (WOM), reuse intention, and the moderating effect of trust in technology. Together, these studies present a comprehensive exploration of how AI can be utilized to transform customer acceptance and optimize retail strategies. This dissertation aims to contribute to a deeper understanding of how AI technologies can be leveraged to enhance retail strategies and customer interactions, offering insights for both academic researchers and industry practitioners.
- Machine learning analysis of antiretroviral procurement strategies in the Mexican government(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-27) Blanca Iveth Mayorga Basurto; Nuñez Mora, José Antonio; emimmayorquin; Moncada Freire, Galo José; Fuentes Castro, Hugo Javier; Carrasco Acevedo, Guillermo; Amorós Espinosa, José Ernesto; EGADE Business School; Campus Ciudad de México; León Alvarado, Martha AngélicaThis dissertation investigates trends in antiretroviral medication (ARV) prices and their impact on public health in Mexico during 2019. The study leverages a dataset comprising 15,220 procurement records collected between 2016 and 2019 to analyze price fluctuations and predict their implications for healthcare systems. Using machine learning models developed in Python-Logistic Regression, Ramdom Forest, and K-Nearest Neighbors (KNN)-this research identifies patterns of increasing and decreasing prices and the factors influencing these trends. The data preprocessing phase involved extensive cleaning, imputation of missing values, feature scaling, and one-hot encoding to handle categorical variables. The dataset was partitioned into training and testing sets using an 80/20 split, ensuring robust validation. Hyperparameter optimization techniques, including grid search and cross-validation, were applied to enhance model performance. The integration of ensemble methods, as exemplified by Ramdom Forest, enabled the capture of complex, non-linear relationships between variables, a critical advantage over simpler models. KNN provided complementary insights into local price clusters, while Logistic Regression offered interpretable coefficients for key predictors. In addition to predictive modeling, the study incorporates a financial evaluation of ARV price fluctuations, estimating the budgetary impact on public health systems. Consolidated purchasing schemes were found to yield significant cost reductions, enhancing access to ARVs for individuals living with HIV/AIDS. A unified ARV pricing database was developed, integrating fragmented data from government procurement systems, ensuring transparency and facilitating reproducibility in future research. This research underscores the transformative potential of data-driven approaches in optimizing pharmaceutical procurement. It highlights the necessity of leveraging machine learning techniques not only for predictive analytics but also for informed decision-making in public health policy.
- Synthesis and Characterization of FAPbI3 Perovskite and its Incorporation into a Photovoltaic Heterostructure(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10) Miró Zárate, Jorge Luis; Elias Espinosa, Miilton Carlos; emimmayorquin; Rosas Meléndez, Samuel Antonio; Melo Máximo, Dulce Viridiana; Flores Ruíz, Francisco Javier; School of Engineering and Sciences; Campus Ciudad de México; Diliegros Godines, Carolina JananiConsidering the importance of having the α-FAPbI3 as it is the photoactive and functional phase for the use of this perovskite in a solar cell and understanding the growth process by incorporating an additive. In this work, it is presented a methodology that combine a method for deposition called sequential deposition with the incorporation of a pseudo halogen additive NH4SCN at various concentration of moles into the PbI2 solution, in order to have α-FAPbI3 perovskite deposited at open atmosphere. This research focuses on the mechanisms of growth of the FAPbI3 perovskite films over glass with the NH4SCN additive. Subsequently, the incorporation of the FAPbI3 perovskite into a heterostructure is presented. The architecture FAPbI3/ETL/ITO/Glass is presented, where the ETLs used are TiO2 and SnO2. The incorporation of FAPbI3 into a heterostructure allows us to evaluate the perovskite's properties for its photovoltaic application. Based on the outstanding electrical properties, WS2 was incorporated into the heterostructure through interface engineering, forming the heterostructure FAPbI3/WS2/ETL/ITO/Glass. Both architectures are compared in terms of their optoelectronic and morphological properties to determine the best FAPbI3-based heterostructure for improved photovoltaic application.
- Instant deliveries in Mexico City: a socio-economic analysis and profit maximization framework for couriers(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-27) Galindo Muro, Ana Bricia; Mora Vargas, Jaime; emipsanchez; Dablanc, Laetitia; Ugalde Monzalvo, Marisol; De Unanue Tiscareno, Adolfo Javier; School of Engineering and Sciences; Campus Ciudad de México; Cedillo Campos, Miguel GastónThis thesis introduces an engineering approach to understanding instant delivery operations within the platform economy. During the first step, through two surveys, the study highlighted couriers’ significant risks and challenges, shedding light on their precarious working conditions and financial pressures. The results emphasize the glaring disparity between the platform economy’s promise of flexibility and independence and the harsh reality experienced by most couriers. Furthermore, the study presents an assignment model to support technological advancements, which can lead to more effective decision-making, benefiting all actors involved in the urban instant delivery platform. By incorporating a fee algorithm and operational cost calculations, the quantitative model developed in this study demonstrates that a 20% increase in couriers’ income compared to traditional assignment models is advantageous for all parties. This approach seeks to raise awareness about the socioeconomic implications of emerging technologies such as Instant Deliveries and their regulation, particularly in rapidly developing urban areas. It offers valuable insights to build a more socially responsible and environmentally sustainable optimization approach in engineering.
- From classical to quantum machine learning for analyzing and predicting alumni outcomes(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Ramos Pulido, Sofía; Hernández Gress, Neil; Torres Delgado, Gabriela; Hervert Escobar, Laura; Garza Villarreal, Sara Elena; Méndez Hinojosa, Luz Marina; School of Engineering and Sciences; Campus Monterrey; Ceballos Cancino, Héctor G.This thesis is submitted to the graduate program at the School of Engineering and Sciences as part of the requirements for obtaining the degree of Doctor of Philosophy in Computer Science. This study aims to generate models using both classical and quantum machine learning (ML) methodologies to accurately predict three key outcomes for alumni: job level, career satisfaction, and first employment. The data analyzed comes from Tec de Monterrey university alumni surveys. The study’s objectives also include the identification of important and actionable features for alumni outcome predictions. Among the challenges in finding models to predict and explain alumni outcomes, we encountered issues such as handling imbalanced classification, hyperparameter tuning, model prediction interpretation, and long training times. To address the latter, we proposed a method that reduces execution time when working with large datasets, particularly in methodologies like support vector machines. This proposal effectively resolves time and memory limitations in high-dimensional classification problems without compromising performance accuracy. The results show that classical machine learning models accurately predicted alumni outcomes. For instance, gradient boosting was most accurate in predicting job level and career satisfaction, while support vector machines outperformed in employment prediction. Significant features identified included current salary and number of people supervised for job level, with higher salaries and more supervisory responsibilities correlating with higher job positions. For career satisfaction, life and income satisfaction were important indicators, as higher satisfaction levels in these areas predicted greater career satisfaction. In the case of employment, networking support resulted as the most important feature, with stronger professional connections significantly increasing the likelihood of securing employment shortly after graduation. Additionally, the research identified actionable features that can impact both educational institutions and students. For job level, soft skills, particularly communication and teamwork, were found to be crucial in advancing to higher positions. Institutions can focus on enhancing these skills through their programs, while students are encouraged to develop them actively. For career satisfaction, the effective use of skills and technological tools acquired during education was a strong predictor, indicating the importance of aligning academic training with the demands of the job market. Facilitating robust professional networks proved essential for employment, emphasizing the need for institutions to create networking opportunities and for students to build social connections proactively. Many more interesting trends and findings related to alumni outcomes are highlighted in the thesis. Regarding quantum machine learning (QML) models, this research demonstrates the v feasibility of predicting alumni outcomes. A hybrid quantum-classical approach was particularly effective in predicting the three alumni outcomes in reduced datasets without substantially affecting accuracy. For example, quantum support vector classifiers (QSVC) showed comparable performance to classical support vector classifiers (SVC) while utilizing a reduced dataset versus SVC with complete datasets. Although QML is still in its early stages, this research suggests that QML could become a viable alternative in educational data mining as the field expands.
- Influence of human error and situational awareness in decision-making in complex tasks. Case of study: forklifts operators(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-19) Arias Portela, Claudia Yohana; Mora Vargas, Jaime; emipsanchez; Castillo Martínez, Juan Alberto; González Mendoza, Miguel; Thierry Aguilera, Ricardo; School of Engineering and Sciences; Campus Ciudad de México; Caro Gutiérrez, Martha PatriciaThis dissertation investigates situational awareness (SA) and human errors in logistics operations, using a multiphase and multifactorial approach as an innovative approach. The research responds the question of how SA errors can be assessed, along with their influence on decision-making in complex tasks, by considering a comprehensive HFE approach to various triggering factors. Characterization of the process with ethnography and process mapping, analysis of visual attention with Eye-tracking and retrospective think-aloud (RTA), an Error taxonomy and the bases of a data science approach were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. Analyzing 566 events across 18 tasks, the research highlights eye-tracking's potential by offering real-time insights into operator behavior, and RTA as a method for cross-checking the causal factors underlying errors. Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high cognitive demand tasks such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable and are the most affected by operator risk generators (ORG), representing 42% of events with a risk of incident. The study identifies driving, hoisting and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADAS). This study provides valuable insights for improving safety in logistics operations by proposing a multiphase and multifactorial approach to uncover patterns of attention, perception and cognitive errors, and their impact on decision-making in the logistic field
- The impact of loading-unloading zones for freight vehicles on the last-mile logistics for nanostores in emerging markets(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Mora Quiñones, Camilo Andrés; Cárdenas Barrón, Leopoldo Eduardo; emimmayorquin; Fransoo, Jan C.; Smith Cornejo, Neale Ricardo; Loera Hernández, Imelda de Jesús; School of Engineering and Sciences; Campus Monterrey; Veláaquez Martínez, Josue CuauhtémocEvery year, more than 26 billion deliveries are made globally to serve nanostores, the largest grocery retail channel in the world. At each stop, company representatives face a persistent challenge: finding a place to park. While the problem seems simple, it is remarkably complex and far from easy to solve. In emerging markets, where cities have grown rapidly and often without proper planning, fragmented markets and inadequate infrastructure exacerbate the issue. Multiple stakeholders compete for limited curb space, and the lack of dedicated parking disrupts last-mile efficiency, forcing drivers to either cruise for parking or resort to illegal parking. These behaviors lead to increased vehicle emissions, noise pollution, and additional costs. This dissertation provides key insights into last-mile logistics for nanostores in emerging markets, contributing to academic literature and offering practical implications to address the parking problem. The first study addresses the parking challenges faced by freight vehicles serving nanostores, identifying key factors affecting dwell time efficiency and suggesting operational improvements. In the next study, the focus shifts to the implementation of Loading-Unloading Zones (LUZs) as a targeted intervention, analyzing their impact on reducing air and noise pollution in urban areas. The last study extends this analysis by exploring the effects of LUZs on traffic flow, evidencing how their introduction can improve vehicle speed and reduce congestion in densely populated city streets. Together, these studies provide a detailed exploration of the operational, environmental, and infrastructural challenges of last-mile logistics, while offering concrete strategies to improve urban logistics in emerging markets. This dissertation contributes by expanding the body of knowledge and offering actionable managerial insights with the potential to drive meaningful impact. These include enhancing air quality, reducing noise pollution, lowering carbon emissions, improving traffic flow, and achieving substantial cost savings for companies distributing goods to nanostores in emerging markets.
- A data-driven modeling approach for energy storage systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Silva Vera, Edgar Daniel; Valdez Resendíz, Jesús Elías; Rosas Caro, Julio César; emipsanchez; Escobar Valderrama, Gerardo; Guillén Aparicio, Daniel; Soriano Rangel, Carlos Abraham; School of Engineering and Sciences; Campus MonterreyThis disertation presents a versatile data-driven modeling methodology designed for various energy systems, including battery-based power systems, DC-DC power electronic converters, Lithium-Ion batteries, and Proton-Exchange Membrane Fuel Cells (PEMFC). The proposed approach captures the non linear dynamics of each system by leveraging fundamental measurements and operational data, thus eliminating the need for explicit theoretical models and significantly simplifying the modeling process. Specifically, the methodology allows for the identification of essential parameters by constructing state-space representations that describe both fast and slow system dynamics, which are crucial for accurately modeling transient behaviors and implementing adaptive control strategies. The models were validated across different applications, showing their ability to replicate real system behaviors with high precision. For instance, in the case of DC-DC converters, the models demonstrated an average error deviation of approximately 2% for current signals and 4% for voltage signals, confirming their capacity to track the actual converter dynamics. Similarly, the Lithium-Ion battery models enabled accurate estimation of state of charge (SoC) and opencircuit voltage using a modified recursive least-squares algorithm, achieving close alignment with real discharge curves. In the PEMFC stack modeling, the methodology utilized real-physic model operational data to refine model accuracy, yielding improved predictive capabilities over traditional approaches. These results underscore the efficacy and robustness of the data-driven approach in enhancing the design, control, and optimization of diverse energy systems. By providing a framework that can be readily adapted to different components and configurations, this methodology supports advancements in sustainable energy technologies, enabling the interconnection of multiple energy storage and conversion systems with minimal computational cost and measurement requirements.
- Design and Development of Conducting Polymer and Carbon Nanostructure based Efficient Thermoelectric Materials(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Ebrahimibagha, Dariush; Mallar, Ray; emimmayorquin; Aguirre Soto, Héctor Alán; Niladri, Banerjee; Gallo Villanueva, Roberto Carlos; School of Engineering and Sciences; Campus Monterrey; Datta, ShubhabrataThermoelectric materials present a promising renewable energy technology for directly converting thermal energy into electricity and vice versa. However, their practical application is hindered by low conversion efficiencies, quantified by the dimensionless figure of merit, 𝑍𝑇 = 𝑆 2 𝜎 𝑘 𝑇 , where 𝑆,𝜎, and 𝑘 are the Seebeck coefficient, electrical onductivity, and thermal conductivity, respectively. Achieving a high 𝑍𝑇 is challenging because enhancing one parameter often degrades the others. Various nanoscale strategies have been explored, yet a comprehensive framework for improving 𝑍𝑇 remains elusive. Recently, polymer-based nanocomposites, particularly carbon nanotubes (CNTs) dispersed in polyaniline (PANI), have gained attention due to their flexibility, non-toxicity, and processability, key traits for next-generation flexible electronic devices. Despite this potential, optimizing thermoelectric performance in PANI-CNT systems is complex, as it depends on numerous factors, including CNT dimensions, functionality, and PANI's doping and morphology. This research employs machine learning (ML) and genetic algorithms (GA) to model and optimize the thermoelectric properties of PANI-CNT nanocomposites. By analyzing structural and compositional variables—such as CNT length, diameter, type, and PANI morphology—we identified strategies that enhance electrical conductivity and the Seebeck coefficient while minimizing thermal conductivity. Our ML models revealed that selecting appropriate dopants for PANI and using single-walled CNT (SWCNT) improves overall thermoelectric performance. Multi-objective GA optimization further refined these findings, demonstrating that SWCNTs help reduce thermal conductivity and that CNT length plays a dual role: shorter CNTs decrease 𝑘, while longer ones enhance both 𝑆 and 𝜎. Experimental validation was performed by fabricating PANI-CNT nanocomposite pellets, but achieving high 𝑍𝑇 remained elusive due to limitations in dataset quality and the variability introduced by diverse synthesis techniques. The synthesis method influences PANI dimensionality (e.g., 0D, 1D, 2D) and the morphology of PANI-CNT composites (core-shell vs. dispersed), complicating performance consistency. While the experiments confirmed the general trend of model predictions, they highlighted the necessity of cleaner, more comprehensive datasets for future research. Ultimately, this study lays the groundwork for designing high-efficiency thermoelectric nanocomposites and outlines the next steps in developing more accurate predictive models and synthesis methods for improved thermoelectric performance.
- Advanced modeling techniques in electric vehicles for battery sizing and Vertical Dynamic Control with CARSIM® and ADAMS(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Drivet González, Aline Raquel Lily; Cespi, Riccardo; emipsanchez; Vargas Martínez, Adriana; Lozoya Santos, Jorge de Jesús; School of Engineering and Sciences; Campus Monterrey; Tudón Martínez, Juan CarlosThis thesis addresses the rapidly accelerating shift from internal combustion engine vehicles to electric vehicles (EVs), a transition driven not only by market demands but also by the urgent need to mitigate climate change. As electrification reshapes the automotive landscape, the importance of advanced modeling techniques are essential to accelerate the adoption of EV technologies, ensuring competitiveness, and addressing environmental urgency. This research begins with a review of vehicle dynamics changes, highlighting the challenges and opportunities introduced by this swift transition to EV technology. The first contribution of this thesis is the application of modeling and simulation techniques using CARSIM®where real-world telemetry is used to optimize EV battery performance and battery sizing. This optimization focuses on maximizing efficiency while maintaining safety and reliability. The second contribution is the development of a model for EV suspension systems using ADAMS®which can be a platform to test critical dynamic behavior of EVs under various conditions. Together, these contributions advance the design and performance of electric vehicles, introducing advanced modeling tools to accelerate development processes, speeding design processes, and addressing the urgent challenges of vehicle electrification in the context of climate change. As a result of the research presented in this thesis, which includes methodologies for battery pack design and the modeling and control of active suspension systems for electric vehicles, two journal articles have been published, and four additional articles have been presented in conference proceedings, contributing significantly to the academic discourse in these areas.
