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
- Hyper-heuristic Model Based on Neural Networks for Solving the Metaheuristic Composition Optimisation Problem in Continuous Domains(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) Tapia Avitia, José Manuel; Terashima Marín, Hugo; emimmayorquin; Pillay, Nelishia; Ortiz Bayliss, José Carlos; Amaya Contreras, Iván Mauricio; School of Engineering and Sciences; Campus Monterrey; Cruz Duarte, Jorge MarioMetaheuristics (MHs) have been proven to be powerful algorithms for solving highly non-linear and intricate optimisation problems over discrete, continuous, or mixed domains, with applications ranging from basic sciences to applied technologies. Nowadays, the literature is prolific with MHs based on outstanding ideas, but the researchers often recombine elements from other methods. To avoid the frenetic tendency of proposing methods more focused on metaphors than operations, a standard model has been proposed to customise population-based MHs, which uses simple heuristics or search operators extracted from well-known metaheuristics. The framework corresponding to this model can be found as Customising Optimising Metaheuristic via Hyper-heuristic Search (CUSTOMHyS), which facilitates implementing models that explore a heuristic space. Still, they are limited by the nature of the metaheuristics used in such models, as such algorithms does not consider the information gained from previous explorations to enhance the tailoring process. A field of action and improvement that has not been explored is the model implementation to take advantage of previous results and learns from them to boost the performance of the tailoring process. For that reason, we propose a hyper-heuristic model based on neural networks, which is trained with processed sequences of heuristics to identify patterns that one can use for generating modified metaheuristics. Being more specific, the task assigned to the neural network is to predict the simple heuristic from the collection or heuristic space to apply next, considering a sequence of heuristics already applied to the low-level problem. Using the neural networks, the challenge is to define how to generate metaheuristics with a high performance for tackling a family of optimisation problems. This research work propose a novel methodology that decomposes the metaheuristics into several subsequences of heuristics to train the neural network models. To prove the feasibility of the proposed model and training methodology, it is compared against generic well-known basic metaheuristics and other heuristic-based approaches, such as the unfolded MHs. The results evidence that the proposed model outperform an average of 86% of all scenarios; in particular, 91% of basic and 81% of unfolded approaches. Plus, it is worth to highlight the configurable capability of the proposed model: several experiments are carried out to explore a few control variables and show their effects in the model. It proves to be exceptionally versatile regarding the computational budget. After exploring and finding a suitable configuration, we perform an extended analysis of the training computational cost, and a study of the metaheuristics generated by the model. Moreover, we analyse the usage of previously generated metaheuristics on an unseen problem via a few strategies. The proposed model and its metaheuristics show their adaptation capabilities to unseen problems, proving to be a good alternative for real-world application problems.
- Modelling, designing and PEST analysis implementation of a porcine products supply chain: a metaheuristics approached solved case study(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022) Elizalde Camacho, Álvaro Ronaldo; Smith Cornejo, Neale Ricardo; puelquio, emimayorquin; Leal Coronado, Mariel Adriana; Escuela de Ingeniería y Ciencias; Campus Monterrey; Hajiaghaei Keshteli, MostafaThere is evidence that supply chain design plays a critical role in the development of competitiveness among companies and industries. Furthermore, the new trends and purchasing behavior exhibited by customers call for the improvement of the supply chain’s performance and mitigation of negative environmental impacts, resulting in the design of varied supply chains, including closed-loop supply chains as they comprise forward and backward flow of products, information, and finance. In this paper, a porcine closed-loop supply chain design for pig and porcine products is proposed due to pig meat and pork products importance thanks to their nutritional value among other agri-food products. Hereby, a multi-period, multi-product linear mathematical model is developed to explain the behavior and minimize the net present value as well as the total cost of the proposed network. To give solution to the model, a set of metaheuristics is used, comprising single-solution, population-based and hybrid algorithms. In addition, a set of 15 trial cases is formed to validate the model. Furthermore, a case study is built for the determination of the parameters to feed the model. The study’s results are compared through the mean relative percentage deviation and convergence curves. According to the results, all used metaheuristics can provide solutions to the proposed model and network, but the metaheuristic H_KASA outperforms all the others in terms of objective function quality values and number of iterations. Finally, managerial insights regarding transportation cost, inventory cost, production capacity and demand are developed.
- BMV stocks return prediction using macro economics variables, technical analysis, and machine learning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-04-01) Hinojosa Alejandro, Ramón; TREJO RODRIGUEZ, LUIS ANGEL; 59028; Trejo Rodríguez, Luis Ángel; puemcuervo; Hervet Escobar, Laura; School of Engineering and Sciences; Campus Monterrey; Hernández Gress, NeilHistorical data, macroeconomic variables, technical analysis, and machine learning are some of the tools used to predict the price of shares of companies listed on the Mexican stock ex-change.The present thesis’s purpose is to reach a robust investment strategy, capable of coping with unforeseen events, and maximizing returns by selecting stocks quoted in the Mexican Stock Market. Our strategy predicts stock returns considering the influence of macroeconomic variables filtered by a causal analysis to determine the most significant ones, and a layered architecture, where machine learning methodologies are endowed with technical analysis applied to the stock historical data.The results from this thesis work show profitable strategies that outperform the free-risk rate of return and the Mexican Index performance. Results demonstrate even good performances when unforeseen events are present as the Covid-19 pandemic in 2020-2021.