Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
Browse
Search Results
- Automated design of specialized variation operators using a generation hyper heuristic for the multi objective quadratic assignment problem(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Morales Paredes, Adrián Isaí; Terashima Marín, Hugo; emimmayorquin; Falcón Cardona, Jesús Guillermo; School of Engineering and Sciences; Campus Monterrey; Coello Coello, Carlos ArtemioThe development of specialized, domain-specific operators has significantly enhanced the performance of evolutionary algorithms for solving optimization problems. However, creating such operators often requires substantial effort from human experts, making the process slow, resource-intensive, and heavily reliant on domain knowledge. To overcome these limitations, generation hyper-heuristics provide a framework for automating the design of variation operators by evolving combinations of heuristic components without direct expert input. In this work, a generation hyper-heuristic method based on grammatical evolution using the hypervolume indicator (HV) as part of its selection mechanism to automatically design variation operators (crossover and mutation) tailored to the multi-objective quadratic assignment problem (mQAP)—a challenging combinatorial optimization problem with many real-world applications—is proposed. The proposed method was used to generate variation operators following a grammar-defined search space. This generation was guided by six mQAP instances featuring 10, 20, and 30 variables with two and three objectives, leveraging MOEA/D as its multi-objective optimizer. During the generation process, the hyper-heuristic exhibited consistent improvements in the HV of the population throughout the evolutionary process, demonstrating its ability to evolve increasingly effective operators over time. To validate the hyper-heuristic output, the generated operators were evaluated on sixteen unseen and diverse mQAPinstances. From experimental results, the evolved operators consistently outperformed standard ones regarding median HV in all test instances. Statistical tests further indicate that these evolved operators possess strong exploration and exploitation capabilities for problems with permutation-based solution representations and behave distinctly from conventional operators. Pairwise comparisons confirmed their superiority over human-designed recombination operators such as PMX and CX in HV performance. These results highlight the potential of automated operator design in effectively solving complex combinatorial optimization problems, such as the mQAP. Beyond this specific case, the proposed framework contributes to the field of automated operator design by introducing a new methodology applicable to multi-objective combinatorial optimization scenarios.
- Hardware-aware neural architecture search for enhancing text generation(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Sánchez Miranda, Israel; Sosa Hernández, Víctor Adrián; emipsanchez; Castillo Juárez, Esteban; Ortiz Bayliss, José Carlos; Juárez Gambino, Joel Omar; School of Engineering and Sciences; Campus Estado de México; Pescador Rojas, MiriamIn recent years, neural network optimization has become critical in Natural Language Processing (NLP) tasks. However, manual tuning processes are time-consuming and heavily influenced by the designer’s prior knowledge, limiting the exploration of alternative architecture designs. Consequently, only a narrow subset of neural network architectures is typically considered for tasks such as text generation. Furthermore, neural network tuning requires specialized expertise, posing a barrier for non-experts and hindering broader innovation in the field. This research addresses these challenges by implementing a specialized Hardware-Aware Neural Architecture Search (HW-NAS) methodology, tailored specifically for text generation tasks under resource-constrained environments. The proposed NAS approach leverages a compact, efficient search space encoding key transformer architectural components, while adopting multi-objective optimization to simultaneously maximize text generation quality, measured via the METEOR score, and minimize the parameter count to enhance hardware adaptability. Two different evolutionary-based NAS strategies were explored: a custom Lexicographic Evolutionary Strategy (LexSMS-MODES) and SMS-EMOA, focusing on balancing exploration, exploitation, and computational efficiency. Experimental evaluations were conducted in both unconstrained environments and constrained hardware platforms. The optimized architectures demonstrated consistent improvements over the baseline model across multiple performance measures, including BLEU, ROUGE, and GLEU. Notably, METEOR scores showed values close to 0.72 in unconstrained settings. Although significant performance degradation was observed under constrained environments (approximately 57%–59% reduction in METEOR scores), the discovered models maintained a competitive edge when compared to several state-ofthe-art light-weight and NAS-based solutions. Hardware-aware evaluations revealed that NAS-generated models achieved substantial reductions in memory usage, GPU load, and CPU frequency deltas, despite not explicitly optimizing hardware indicators during the search. Statistical tests confirmed the stability of the discovered models across multiple hardware performance metrics. Comparisons against external works showed that while the proposed method successfully produced light-weight and efficient architectures, there remains room for improvement regarding inference latency and hardware adaptation strategies.
- Optimization of kinetic and operating parameters in bioreactors using evolutionary algorithms(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Barrera Hernández, Gonzalo Irving; Sosa Hernández, Víctor Adrián; emipsanchez; Alfaro Ponce, Mariel; Aranda Barradas, Juan Silvestre; Corrales Muñoz, David Camilo; School of Engineering and Sciences; Campus Estado de México; Gómez Acata, Rigel ValentínBioreactors play a role in creating biological products such as medicines and biofuels by care fully controlling factors such as substrate levels and temperature within them to obtain optimal production results, bioreactor production process poses a challenge that poses a challenge to engineers due to the intricate setup involved. In the field of microbiology and biotechnology, conventional approaches such as the Monod model, logistic growth models, and fed-batch techniques have been employed to predict and improve the growth conditions of microor ganisms and the production of proteins of interest in fermenters. However, these approaches could face challenges when they encounter nonlinear systems and conflicting objectives. To address these challenges, our suggestion is to approach the configuration of factors in bioreactors as an optimization problem using an evolutionary algorithm that can improve the effectiveness and quality of the operating process. The objective of this study is to in vestigate and create a pipeline that integrates evolutionary algorithms to solve multi-objective and scalar optimization problems, aimed at identifying kinetic and critical parameters within a bioreactor system. The optimization process involves, in the first stage, a least squares ap proach that considers product, biomass, dissolved oxygen, and substrate concentrations as objectives, with the kinetic parameters (e.g., maximum specific growth rate and substrate affinity) serving as decision variables. The second stage focuses solely on maximizing the amount of produced product, specifically biomass, using critical operational variables, such as feed rate and aeration, as decision variables. The research employs Escherichia coli as a microorganism that has been genetically al tered to produce orange fluorescent protein (OFP) to test the validity of improvement frame works. Initially, in the simulation and process tuning phase, experimental information, from batch cultures, is used to accurately determine the factors. Later, in the fed-batch phase, the application of an algorithm is used to optimize biomass yield while considering operational constraints such as oxygen levels and maximum reactor volume. The findings show that this method accurately calculates factors during the fed-batch phase and efficiently increases biomass production in the continuous fed phase. The use of algorithms such as multiple NSGA-III and single-objective genetic algorithms provides valuable benefits when dealing with intricate bioreactor configurations that have conflicting objectives such as managing substrate consumption and improving production yield. This approach has promising prospects for improving the accuracy and efficiency of bioprocess optimization, while increasing its scalability, in the field of biotechnology in the future.
- An improved multi-objective optimization problem model for enhancing UAV path planning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Castañon Guerrero, Franco; Sosa Hernández, Víctor Adrián; mtyahinojosa; Yee Rendón, Arturo; Estrada Delgado, Mario Iván; Escuela de Ingeniería y Ciencias; Campus Estado de México; Becerril Gómez, Jorge AntonioUnmanned Aerial Vehicles (UAVs) have become crucial in various industries, such as agriculture, construction and mining, infrastructure inspection, environmental monitoring, and emergency response. These diverse applications underscore the importance of UAV drone path planning for enhancing efficiency and safety. This work builds upon the study presented by Wang et al., highlighting limitations in environmental modeling. The failure to accurately replicate the environmental conditions can be attributed to insufficient documentation of the modeling methodology, hindering the repeatability and robustness of the findings. Critiques also target the fitness functions lacking theoretical grounding. The Threat Index assesses flight smoothness but lacks clear operational descriptions, while the Concealment Index evaluates safety but suffers from unclear procedures. There is a need for an improved and accurate model. Our contribution introduces two new functions for UAV path planning, optimizing the two distinct aspects: the Threat and Concealment of the trajectory. The first proposed function focuses on the distance of the path to the surface, incorporating altitude variations and terrain features to minimize deviations from the surface. The second one addresses angular preferences, minimizing deviations from a straight-line trajectory to reduce the impact of inertia on UAV dynamics. The integration of the new objective functions contributes to a multi-objective optimization framework, balancing considerations of path proximity to the surface and path linearity for enhanced UAV path planning performance. Our framework involved conducting four test scenarios with distinct points of origin and goals, utilizing the SMS-EMOA algorithm to find the best path. Each experiment was characterized by unique initial and terminal coordinates, allowing for a comprehensive evaluation across diverse scenarios. The evolutionary algorithms were configured with specific parameters to balance computational efficiency with optimization robustness. Additionally, 30 independent runs were performed for each scenario, comparing the two sets of objective functions to capture the general behavior of each one. The success of the experiments was measured by the convergence of the algorithms towards Pareto-optimal solutions, demonstrating adaptability and effectiveness across varied spatial scenarios. Wang et al.’s framework contrasts with ours by focusing on comparing the performance of an improved NSGA-II algorithm adapted to the problem context. Their analysis revealed that their improved NSGA-II algorithm outperformed NSGA-II regarding route length and threat reduction, with modest improvements in concealment. Our framework offers a comprehensive and systematic approach to evaluation. Through multiple experiments across diverse scenarios and specific parameters, it provides a thorough understanding of algorithm performance under various conditions.
- Multi-objective optimization of an integrated biofuel and power production network from Municipal Solid Waste (MSW) for Latin America(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12-06) Niño Caballero, Javier Camilo; Montesinos Castellanos, Alejandro; puemcuervo, emimayorquin; López Guajardo, Enrique Alfonso; School of Engineering and Sciences; Campus Monterrey; Hernández Romero, Ilse MaríaDespite significant advances in waste management, there are still many challenges that require special and immediate attention. Such as, for example, the existence of uncontrolled open dumps, low recovery rates of waste fractions, expensive and inefficient treatments, and the environmental impact. Therefore, in this work, a multi-objective optimization is proposed to determine the optimal operation policy that considers the energetic valorization of MSW for Latin America, specifically Argentina, Brazil, Colombia, and Mexico. The optimization formulation considers the analysis of four technologies: anaerobic digestion, incineration, gasification, and pyrolysis, and it was developed, including the technical, economic, and environmental dimensions. This work addresses an approach for defining the relationships between the operational policies and the design of MSW management considering the above technologies. The solution to the problem consists of maximizing profit and the net emissions avoided from the operation of the technologies simultaneously. In addition, to attack the multi-objective problem, the epsilon-constraint method was used to find the optimal solutions. In addition, a trade-off solution is selected to present the main results associated with the operational policy of each country and as well with each proposed technology. Minimum sizes for the profitability of each technology were obtained for each country, and it was determined that Colombian plants and incineration technology require the lowest MSW ton managed to obtain profits opposing Brazil plants and pyrolysis technology. The model showed flexibility with the objective correlation, concluding that the economic and environmental objective functions are not necessarily in opposition. The present model is a general model that can be applied to another country or group of countries, considering the exact specifications.
- Designing a mathematical model and solution for a circular economy-based closed-loop supply chain in semiconductor materials, focusing on microchips and motherboards(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-08) Retamoza Salazar, Mariana; Smith Cornejo, Neale Ricardo; emimmayorquin; Esturí, Shiva; Escuela de Ingeniería y Ciencias; Campus Monterrey; Hajiaghaeikeshteli, MostafaAs environmental concerns escalate and societal expectations evolve, the imperative for sustainable practices within supply chain management becomes increasingly evident. Closed- oop Supply Chain (CLSC) emerges as a pivotal strategy, aiming to integrate environmental sustainability with economic efficiency. This research addresses the pressing need for nnovative solutions in the semiconductor industry, focusing on microchips and motherboards, where the traditional linear supply chain odel falls short in mitigating environmental impact and demand fulfillment. This study pioneers the development of a CLSC networking framework explicitly tailored for semiconductor products. A comprehensive multiperiod multivariable mathematical model is proposed for extraction and manufacturing, as well as customer delivery, collection, recycling, and reintegration.This model pursues a bi-objective: minimizing the total costs of the network and reducing greenhouse gases, particularly the Global Warming Potential (GWP), thus advancing economic and environmental sustainability. Utilizing the General Algebraic Modeling System (GAMS) with Mixed-Integer Programming (MIP), the proposed model is solved, providing actionable insights for sustainable supply chain management. Moreover, a case study in Mexico offers practical application and further explains the model's efficacy. Finally, managerial insights are derived through an LP-metric Method, enabling a comparative analysis of the dual objective functions. This facilitates informed decision-making, emphasizing integrating economic and environmental considerations in supply chain management strategies. In conclusion, this research contributes to the advancement of sustainable supply chain practices within the semiconductor industry, offering a comprehensive framework for decision-makers to navigate the complexities of modern supply chain management.
- Diseño y aplicación de un modelo de localización asignación para la evaluación del acceso a unidades médicas de primer nivel de la Secretaria de Salud para municipios rurales en el Estado de México(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2012-05-04) Mendoza Gómez, Rodolfo; SERRATO GARCIA, MARCO ANTONIO; 240230; Valenzuela Ocaña, Karla B; emipsanchez; Serrato García, Marco Antonio; Robles Cárdenas, Manuel; Escuela de Ingeniería y Ciencias; Campus Toluca; Ríos Mercado, Roger Z.To face the problems of access to health services in Mexico, the purpose of this work is to present a tool for making decisions regarding the problem of the population proportion with limited access to health services, being more significant in rural areas. Concerning this, the Integrator Model of Health Care of the Health in Mexico is aimed at removing geographical barriers, as well as improving organizational and cultural access to health services through the coordination of health care networks. In this work, in order to improve the formation of these networks, we propose the use of models for allocating the population to different public health facilities and the enlargement or location of new ones when the capacity is insufficient. With information available to Mexico, the models were applied in five rural municipalities in Mexico State. The results were compared with the Operational Regionalization Study of Mexico State in 2001 and the Physical Infrastructure Master Plan in Health for Mexico State in 2010, with a significant reduction in distance traveled by the population, better equitable distribution in the different health facilities, and identification of candidate sites for opening new units were found, maximizing the coverage and improving the level of service.