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.
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- 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.