Ciencias Exactas y Ciencias de la Salud

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Exploring data-driven selection hyper-heuristic approaches for the curriculum-based course timetabling
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12) Hinojosa Cavada, Carlos Alfonso; CONANT PABLOS, SANTIAGO ENRIQUE; 56551; Conant Pablos, Santiago Enrique; emipsanchez; Ortiz Bayliss, José Carlos; School of Engineering and Sciences; Campus Monterrey
The curriculum-based timetabling problem (CB-CTT) represents a challenging field of study within educational timetabling, with real-world applications that stress its importance. Solving a CB-CTT problem requires allocating a set of courses using limited resources, subject to a set of hard constraints that must be satisfied. The goal then is to find a feasible assignment for every lecture that constitutes the courses to the positions in the timetable formed by a combination of day, period, and room; all while minimizing an objective function as specified by the constraints in the problem. Designing the timetable for the courses in the incoming term is a problem faced by universities each academic period. Given the complexity of manually designing timetables, automated methods have attracted the attention of many researchers for solving this problem. The design of timetables remains an open problem to this day. According to the no free lunch theorem, different heuristics are effective on different problem instances, stressing the importance of finding automated methods for designing timetables. This dissertation explores novel hyper-heuristic models that rely on various machine learning techniques, such as boosting, clustering and principal component analysis. In total, two models were designed and implemented as results of this work. The first model relies on gradient boosting algorithms to generate a selection hyper-heuristic. The general idea is that different instances of the CB-CTT are best solved by different heuristics. Hence, the aim is to create a model that learns from the features that describe problem instances and predicts which would be the most suitable heuristic to apply. While the classification model produces promising results in terms of accuracy, the quality of the generated solutions is bounded by the best-known single heuristic. The second model aims to remove the bounds set by the use of a single heuristic by exploring ways of combining heuristics during the timetable construction process. The selection hyper-heuristic approach is powered by principal component analysis and k-means. The model starts by identifying similar regions in the instance space and keeping track of the performance of each heuristic for those regions. Then, when constructing new timetables, the model determines the most suitable heuristic for a given region of the instance space. The method was able to outperform the synthetic oracle created by taking the result of the best isolated heuristic in several instances. This dissertation is submitted to the Graduate Programs in Engineering and Information Technologies in partial fulfillment of the requirements for the degree of Master of Science in Computer Sciences with a major in Intelligent Systems.
Scheduling in Ad-Hoc Networks
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2004-01-08) Cabrera Rodríguez, Tathiana; Vargas Rosales, César; Rodríguez Cruz, José Ramón; Mex Perera, Jorge Carlos; ITESM
La calendarización (Scheduling) de los recursos en las redes inalámbricas es muy importante en estos días. Muchos autores han desarrollado mÚltiples algoritmos para una mejor administración de dichos recursos. Los algoritmos que se han propuesto hasta ahora han abordado diferentes recursos y/o parámetros de desempeño, tales como: retardo, throughput, potencia, acceso al canal, etc. En éste trabajo abordaremos el control de potencia. Su propósito primario en las redes celulares es atenuar el efecto near-far, y por lo tanto reducir la degradación del desempeño causada por interferencia mÚltiple de acceso. Mientras que para las redes Ad hoc ayuda a lograr un objetivo similar, asume importancia adicional, ya que la vida finita de la batería es un asunto clave para los nodos móviles de dichas redes. Esta investigación estudia a un algoritmo conjunto de scheduling y control de potencia, que ayuda a eliminar las posibles interferencias que los nodos vecinos pueden ocasionar en una transmisión principal, así como la manera de minimizar el consumo de potencia de transmisión, todo esto con el propósito de alargar la vida de la batería.
Evaluación del nivel de desempeño en el scheduling aplicando la metodología Seis Sigma
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2002-05-01) Arroyo Calderón, Mónica; García Lumbreras, Salvador; Viramontes Brown, Federico; Mireles Gaytán, Francisco; Pulido Córdoba, Benjamín; Castro Ugalde, Felipe D.; Programa de Graduados en Ingeniería; División de Ingeniería y Arquitectura; Campus Monterrey
Agent scheduling in unrelated parallel machines with sequence and agent-machine dependent setup times
(Instituto Tecnológico y de Estudios Superiores de Monterrey) Vázquez Serrano, Jesús Isaac; PEIMBERT GARCIA, RODRIGO ERNESTO; 226983; Peimbert García, Rodrigo Ernesto; puelquio, emipsanchez; Smith Cornejo, Neale Ricardo; Escuela de Ingeniería y Ciencias; Campus Monterrey; Cárdenas Barrón, Leopoldo Eduardo
The assignation-sequencing models have played since the mid 50’s a critical role in economic competitivity within companies and organizations. Models have evolved from simple assignations to complex constrained formulations, and still is an area of deep interest to researchers. This thesis presents a better representation of the reality regarding assignation-sequencing models, a model to schedule agents in unrelated parallel machines with sequence and agent-machine dependent setup times (ASUPM), with minimization of maximum makespan criteria and a relationship one agent to one machine. Six mixed-integer linear formulations are proposed, and since the problem complexity belongs to the NP-hard category, two heuristic algorithms are presented. The linear models and heuristics algorithms are tested using randomly generated instances. In addition, it is presented the inclusion and solution of other assignation-sequencing models in the/with ASUPM problem. Finally, two applications of the model are presented: scheduling in automotive seat safety tests, and hemodialysis scheduling in hospitals.

Ciencias Exactas y Ciencias de la Salud

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