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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- Routing and storage assignment for the precedence-constrained order picking process(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06-20) Pineda Romero, Valeria Viridiana; REGIS HERNANDEZ, FABIOLA; 331834; Regis Hernández, Fabiola; puelquio/mscuervo; Espinoza García, Juan Carlos; School of Engineering and Sciences; Campus Monterrey; Murrieta Cortés, BeatrizOrder picking is retrieving items from the warehouse to fulfill customers’ orders. It is considered the most labor-intensive and time-consuming operation in a warehouse and composes almost half of the total operating costs. Thus, developing efficient routing sequences for order pickers has been one of the main focus projects of managers. In addition, in real-warehouse environments, routing is frequently influenced by precedence constraints. Precedence constraints arise when certain products need to be collected before others due to a particular physical characteristic of the items. For instance, precedence constraints may be defined by the products’ fragility, weight, or size, among others. Even though many warehouses face such constraints, they have often been neglected in the scientific literature. This dissertation is inspired by a practical case of a Mexican Company that stores perishable products, which are considered sensitive items; this means that they are easily deformed if a certain weight is placed on them. This situation arises the problem that the warehouse under study must consider Unit of Measurement and Load constraints. The Unit of Measurement constraint prevents box-packed items from being placed on top of individual units. Load constraint allows only a limited number of boxes to be placed on top of another box. To develop a solution to this concern, we propose a mathematical model to formulate the problem. Due to its complexity, the implementation of an approximate method was mandatory. Indeed, a Genetic Algorithm was designed to meet this problem’s requirements. In addition, we propose three Storage Assignment strategies to analyze if these further improve the traveling distance of the resulting routing sequences. These were applied to a set of instances obtained from the Company’s Warehouse Management System observations. We assess the picker routing and storage assignment strategies’ performance and obtain essential knowledge for this type of problem.
- A data analytics approach for university competitiveness: the QS rankings(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06) Estrada Real, Ana Carmen; ESTRADA REAL, ANA CARMEN; 791773; Cantú Ortiz, Francisco Javier; emipsanchez/puemcuervo; Sucar Succar, Luis Enrique; Galeano Sánchez, Natalíe María; Hernández Gress, Neil; Monroy Borja, Raúl; School of Engineering and Sciences; Campus Estado de México; Ceballos Cancino, Héctor GibránIn recent years, higher education has been facing the entrance to the internationalmarket due to globalization, this has developed a highly competitive environment, in whichmany institutions have used university rankings as a tool to attract the best academic andstudent talent from all over the world. In this work we take as a base the ranking of QSWord University Rankings and QS Best Student Cities, to apply data science techniques.Extract information on the performance of the most attractive institutions and cities forstudents worldwide, and develop a methodology that allows the stakeholders of the insti-tutions and cities to improve their services for the benefit of students interested in receivingan education of global quality. We accumulated ten years of university rankings (2011-2020) and six years of city rankings (2014-2019), we carried out an exploratory analysisof the indicators and their influence with the final score, later we trained a multiple regres-sion model and panel data to make predictions in the score. Finally, in order to predictthe position, we carry out groupings and train various machine learning algorithms. Withthis work we show a methodology that allows administrators to plan long-term institutionalimprovements to offer a better education and improve their performance in world rankings.