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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- Price and due date quoting for a mass customization company(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-15) De la Torre Altamirano, Mario Alejandro; Smith Cornejo, Neale Ricardo; lagdtorre/tolmquevedo; Tercero Gómez, Víctor G; Ángel Bello Acosta, Francisco R; Escuela de Ingeniería y Ciencias; Campus MonterreyMass customization is a competitive strategy based on offering a wide variety of options without greatly increasing production costs. Some associated challenges are the management of components inventory and job scheduling. Three unique scheduling mixed integer programming models with sequence dependent setups are proposed for different production scenarios. They allow quoting different sale prices depending on what due date is acceptable to the customer. Three solutions methods were employed, one is a mixed integer programming formulation with the Gurobi optimizer solver, which guarantees to find the optimal solution, but is restricted to the problem size. The others are a simulated annealing and an ant colony heuristic, which does not guarantee to find the optimal answer, but is not restricted by the size of the problem. Both heuristics probe to bring statistically similar results. Each solution method was successfully validated on a real-world mass customization firm. Due date variation analysis showed a high correlation between due date and cost. Model can be meaningfully used since long lead time can be a major cause for market share loss.
- Bioinspired Search and location of Air Pollutant Sources based on a Bayesian Heuristic + Dragonfly Algorithm(Instituto Tecnológico y de Estudios Superiores de Monterrey) García, Andrés; 891216; Garza, Luis Eduardo; Vargas, Adriana; Sotelo, David; Campus Monterrey; Campus Monterrey; Campus MonterreyIn nature there are many types of interaction between individuals. These behaviors have been optimized in millions of years and their main objective is the survival of the species. The objective of interacting with other individuals is to solve difficult problems, problems that a single individual would solve inefficiently. The problem aborted by this thesis is the search for and location of air pollutant sources. A SO2 plume is simulated and placed in a region to perform the search. To collect contaminant samples, the dynamics of 2 unmanned aerial vehicles are simulated. The algorithm runs under a real-time simulation environment. A 2-dimensional Hammersley sequence is used in an exploration stage. Clustering by Kmeans and a Greedy Search are also used to solve the Travelling Salesman Problem at the Hammersley points. In this first stage an attempt is made to find a measure of the concentration of air pollutants that exceeds a certain threshold. In an exploitation stage, the Unmanned Aerial Vehicles (UAVs) will track the source approaching this point. A probability map is used to modify the behavior of the Dragonfly Optimization Algorithm. The probabilistic map acts as the heuristic of the system to locate the source of air pollution. To check the efficiency of the proposed strategy it is compared with 3 other algorithms: Greedy Search, Greedy Search with cooperation and the Dragonfly Algorithm without modifications. All these approaches are tested in the same simulated environment, without obstacles for UAVs. Efficiency is measured with respect to the maximum level of concentration of contaminants found, the distance to the source of contamination, the time taken to find the maximum concentration of contaminants and the UAVs path until that time. Finally, a one-factor ANOVA is performed to check that the responses are statistically better.