Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Geospatial location-allocation optimization for maximum coverage in pharmacy-based immunization strategies(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Romero Mancilla, Marisol Saraí; Mora Vargas, Jaime; emipsanchez; Santos Borbolla, Cipriano Arturo; Regis Hernández, Fabiola; Smith Cornejo, Neale Ricardo; School of Engineering and Sciences; Campus Ciudad de México; Ruiz, Angel(Only 1 page) Since the beginning of the Coronavirus disease outbreak in 2019, around 184 million cases and 4 million deaths have been reported worldwide [101]. The early decisions made by the entities responsible for managing the health emergency in each country were crucial to define their future. Detection with massive population screening tests, containment with the isolation of suspected and infected cases, and preparation of the healthcare system to face the demand were key factors in controlling the transmission of the disease. Therefore, the primary objective of this thesis is to develop an adaptable integrated geospatial model that ensures efficient distribution of vaccines during health emergencies, illustrating this through a case study in Jalisco, Mexico. In order to complete the aforementioned, a literature review on pharmacy-based im- munization was conducted before developing the mathematical approach. Subsequently, a facility location allocation problem and a hybrid approach known as fix-and-optimize were used to solve larger instances, using the Gurobi optimization software. In addition to contributing to the literature on Humanitarian Logistics in health sys- tems, the results will inform national policymakers at the tactical and strategic decision levels about the development of anticipatory governance for managing resources during situations arising from the Covid-19 pandemic.
- Optimization and Simulation of a Defined Contribution Pension System for Faculty Under Certainty and Uncertainty(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-02-11) Montufar Benítez, Marco Antonio; Mora Vargas, Jaime; emimmayorquin; Gónzalez Mendoz, Miguel; School of Engineering and Sciences; Campus Estado de México; Muñoz Negrón, David FernandoIn recent years, the increasing complexity and risk exposure of pension systems have ledresearchers to adopt hybrid approaches that integrate simulation and optimization techniques to support strategic decisions. Simulation methods, particularly Monte Carlo and scenariobased simulations, have been widely applied to capture uncertainties related to asset returns, salary progression, interest rates, and mortality risk (Boulier et al., 1995; Haberman and Vigna, 2002). These simulations enable the modeling of dynamic systems over long horizons and provide insight into the stochastic nature of pension outcomes. Parallel to this, optimization techniques—especially stochastic control, dynamic programming, and quadratic programming—have been employed to derive optimal asset allocations and contribution strategies. For example, Cairns et al. (2006) introduce a stochastic lifestyling model for defined contribution plans, optimizing investment strategies based on salary risk and annuity constraints. Similarly, Consiglio et al. (2015) develop a stochastic programming model to design and price guarantee options in DC plans, balancing cost and embedded risks. Shen and Sherris (2017) extend these methods by integrating stochastic mortality, income, and interest rate models into a unified lifetime optimization framework. Their work illustrates how both idiosyncratic and systematic longevity risks influence investment, consumption, and insurance strategies over an individual’s ifetime. Together, these studies show that combining simulation with optimization provides a robust toolkit for assessing pension policy reforms, designing dynamic investment strategies, and evaluating guarantee mechanisms under demographic and financial uncertainty. This integrated approach enhances the capacity to make informed, resilient decisions that align long-term financial sustainability with retirees’ welfare. This work synthesizes a series of studies addressing pension planning for academic personnel at Mexican higher education institutions. The first study, presented at the 2020 ICPR-Americas conference, introduced a linear optimization model developed in LINGO to determine the minimum salary contribution rate required to ensure retirement coverage over a desired number of years. The model incorporates key economic variables such as inflation, salary increases, and interest rates. Results showed that a contribution rate of approximately 13% was necessary to provide sufficient retirement income over a 10- to 20-year post-retirement period. Building upon this foundation, the 2025 article in Computation presents a deterministic multi-objective optimization model designed to both minimize the ontribution rate and maximize post-retirement coverage. Implemented in LINGO, the model evaluates three realistic economic scenarios under varying inflation rates and retirement needs. It produces Pareto-optimal solutions, revealing that optimal contribution rates can range from 10% to 80% of salary depending on assumptions, particularly inflation and desired income replacement levels. The study published in 2024 in Computational Statistics, applies a stochastic simulation model using Arena software to analyze a defined contribution retirement scheme. The simulation incorporates faculty age, seniority, salary dynamics, and attrition probabilities. The results show that an increase in the number of simulation replicas significantly impacts the precision and stability of average capital estimates. When moving from 30 to 300 replicas, the mean capital increases from 479.79 to 509.46 thousand MXN, and the 90% confidence interval for this mean becomes noticeably narrower, shrinking from a range of [435.56, 524.03] to [495.21, 523.71] thousand MXN. This reduction in interval width is clear evidence of increased reliability in the average capital estimation. When simulating with 1000 replicas, the mean capital further stabilizes at 510.45 thousand MXN, and the 90% confidence interval contracts to an even more precise range of [502.75, 518.15] thousand MXN. This reaffirms that a higher number of simulations leads to a more robust estimate with less uncertainty. A consistent finding across all scenarios is the 5% Value at Risk (VAR 5%), which remains at 27.82 thousand MXN. This indicates that 5% of capital outcomes fall below this value, suggesting stability in the lower capital thresholds, regardless of the number of replicas used. Finally, the Coefficient of Variation (CV), although showing a slight decrease with more replicas (from 108.56% to 102.50%), consistently remains above 100%. This high CV underscores a considerable relative variability in the capital data. This elevated dispersion implies that, while the mean becomes more precise with more replicas, the intrinsic variability of teachers’ capital is a prominent characteristic that must be considered in risk assessment. In summary, the simulation results improve in precision and stability as the number of replicas increases, which is fundamental for informed decision-making within a pension system.. Together, these studies demonstrate the value of combining optimization and simulation techniques to inform institutional pension planning, offering robust tools for decision-makers navigating the evolving landscape of retirement systems in Mexico.
- 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
- Diseño de una metodología para medir el valor logístico de la infraestructura carretera para las cadenas de suministro(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023) Pérez González, Carlos Mario; PEREZ GONZALEZ, CARLOS MARIO; 441757; Mora Vargas, Jaime; emipsanchez; Montiel Moctezuma, Cesar Jaime; Escuela de Ingeniería y Ciencias; Campus Estado de México; Cedillo Campos, Miguel GastónUno de los principales objetivos de la logística, es la generación de valor agregado a partir de los procesos llevados a cabo dentro de la cadena de suministro. Este valor agregado está intrínsecamente ligado a la infraestructura de transporte utilizada por la cadena. Desde este punto de vista, el análisis del desempeño del transporte en la cadena de suministro debe considerar la infraestructura disponible para evaluar el valor agregado por la misma cadena. Así, este valor identificado se define como valor logístico. La definición de este concepto es un punto de partida para la mejora participativa de todos los usuarios. Este planteamiento es de importancia tanto para entidades públicas, como apoyo para mejorar el desempeño de los sistemas de transporte, así como, para organizaciones privadas, en la toma de decisiones sobre localización y uso de rutas de transporte. Recientemente, se propuso una definición y se plantearon atributos basados en tiempo y costo. Dado que el área permanece relativamente inexplorada, aún no existe un consenso sobre que indicadores deben de incluirse en el estudio del valor logístico. De igual forma, no es posible identificar una metodología que valué el desempeño del sistema de transporte de forma operativa y beneficié a todos los usuarios de la cadena de suministro con bases firmes y realistas. En este contexto, para el presente trabajo de investigación se realizó una búsqueda crítica y exhaustiva del estado del arte para identificar los esfuerzos actuales para la medición del desempeño del transporte por carretera en cadena de suministro. Además, considerando que el principal medio de transporte en México es el autotransporte de carga por carretera, esto debido a que, tan solo en el 2020 este representó el 56.9% del total de carga movida en México, de acuerdo a la Secretaría de Infraestructura, Caminos y Transportes, se propone al sistema al sistema de transporte de carga por carretera como el objetivo del análisis. En este sentido, se presenta una propuesta para la evaluación del valor logístico mediante un conjunto de indicadores seleccionados (tiempo, costo, riesgo y conectividad) para evaluar el desempeño de una ruta de transporte. Para la evaluación integral de la cadena se usaron un conjunto de indicadores para la evaluación del desempeño identificados en el análisis de literatura y se propuso un par de indicadores relevantes para todos los usuarios. Dentro de la aportaciones que se alcanzaron con la investigación se encuentran: i)Proponer un conjunto de indicadores a considerar para realizar el análisis del valor logístico del transporte en cadena de suministro; ii) Proponer una metodología basada en indicadores de tiempo, costo, riesgo y conectividad, así como, información histórica, de datos GPS, factores de costo de operación por vehículo y accidentes por tramo, para el cálculo del valor logístico; iii) Presentar conclusiones sobre los resultados de la evaluación, que apoyen la toma de decisiones en organizaciones públicas y privadas,7 para la mejora el desempeño del sistema de transporte y la administración de recursos, respectivamente; y finalmente, iv) exponer líneas futuras de investigación enfocadas a la inclusión de otros indicadores para su evaluación