Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach

Crohn's disease (CD) diagnosis is a tremendouslyserious health problem due to its ultimately effecton the gastrointestinal tract that leads to the need of complexmedical assistance. In this study, the backpropagationneural network fuzzy classifier and a neuro-fuzzy modelare combined for diagnosing the CD. Factor analysis isused for data dimension reduction. The effect on the systemperformance has been investigated when using fuzzypartitioning and dimension reduction. Additionally, furthercomparison is done between the different levels of thefuzzy partition to reach the optimal performance accuracylevel. The performance evaluation of the proposed systemis estimated using the classification accuracy and othermetrics. The experimental results revealed that the classificationwith level-8 partitioning provides a classificationaccuracy of 97.67 %, with a sensitivity and specificity of96.07 and 100 %, respectively

Solution to the generalized lattice point and related problems to disjunctive programming

Issued as Pre-prints [1-5], Progress reports [1-2], Final summary report, and Final technical report, Project no. E-24-67

Problemas de asignación de recursos humanos a través del problema de asignación multidimensional

149 páginas. Doctorado en Optimización.El problema de asignación de personal aparece en diversas industrias. La asignación eficiente de personal a trabajos, proyectos, herramientas, horarios, entre otros, tiene un impacto directo en términos monetarios para el negocio. El problema de asignación multidimensional (PAM) es la extensión natural del problema de asignación y puede ser utilizado en aplicaciones donde se requiere la asignación de personal. El caso más estudiado de PAM es el problema de asignación en tres dimensiones, sin embargo en años recientes han sido propuestas algunas heurísticas de búsqueda local y algoritmos meméticos para el caso general. En este trabajo de tesis se realiza un estudio profundo de PAM comenzando con un resumen del estado del arte de algoritmos, heurísticas y metaheurísticas para su resolución. Se describen algunos algoritmos y se propone uno nuevo que resuelve instancias de tamaño medio para PAM. Se propone la generalización de las conocidas heurísticas de variación de dimensión como una búsqueda local generalizada que proporciona un nuevo estado del arte de búsquedas locales para PAM. Adicionalmente, se propone un algoritmo memético con una estructura sencilla pero efectiva y que es competitivo con el mejor algoritmo memético conocido para PAM. Finalmente, se presenta un caso particular de problema de asignación de personal: el Problema de Asignación de Horarios (PAH). El PAH considera la asignación de personal a uno, dos o más conjuntos de objetos, por ejemplo puede ser requerida la asignación de profesores a cursos a periodos de tiempo a salones, para determinados grupos de estudiantes. Primero, se presenta el PAH así como una breve descripción de su estado del arte. Luego, se propone una nueva forma de modelar este problema a través de la resolución de PAM y se aplica sobre el PAH en la Universidad Autónoma Metropolitana, unidad Azcapotzalco (UAM-A). Se describen las consideraciones particulares del PAH en la UAM-A y proponemos una nueva solución para éste. Nuestra solución se basa en la resolución de múltiples PA3 a través de los algoritmos y heurísticas propuestos.Personnel assignment problems appear in several industries. The e cient assignment of personnel to jobs, projects, tools, time slots, etcetera, has a direct impact in terms monetary for the business. The Multidimensional Assignment Problem (MAP) is a natural extension of the well-known assignment problem and can be used on applications where the assignment of personnel is required. The most studied case of the MAP is the three dimensional assignment problem, though in recent years some local search heuristics and memetic algorithms have been proposed for the general case. Let X1; : : : ;Xs be a collection of s 3 disjoint sets, consider all combinations that belong to the Cartesian product X = X1 Xs such that each vector x 2 X, where x = (x1; : : : ; xs) with xi 2 Xi 8 1 i s, has associated a weight w(x). A feasible assignment is a collection A = (x1; : : : ; xn) of n vectors if xi k 6= xj k for each i 6= j and 1 k s. The weight of an assignment A is given by w(A) = Pn i=1 w(xi). A MAP in s dimensions is denoted as sAP. The objective of sAP is to nd an assignment of minimal weight. In this thesis we make an in depth study of MAP beginning with the state-ofthe- art algorithms, heuristics, and metaheuristics for solving it. We describe some algorithms and we propose a new one for solving optimally medium size instances of MAP. We propose the generalization of the called dimensionwise variation heuristics for MAP and a new generalized local search heuristic that provides new state-of-theart local searches for MAP. We also propose a new simple memetic algorithm that is competitive against the state-of-the-art memetic algorithm for MAP. In the last part of this thesis, we study a particular case of personnel assignment problem: the School Timetabling Problem (STP). The STP considers the assignment of personnel to other two or more sets, for example the assignment of professors to courses to time slots to rooms can be required. First, we provide a brief description of the state-of-the-art for STP. Then, we introduce a new approach for modeling this problem through the resolution of several MAP and we apply our solution on a real life case of study: STP at the Universidad Autonoma Metropolitana campus Azcapotzalco (UAM-A). We provide the particular aspects for STP at UAM-A and we provide a new solution for this problem. Our approach is based on solving several 3AP considering the introduced model and our proposed techniques.Consejo Mexiquense de Ciencia y Tecnología (Comecyt).Consejo Nacional de Ciencia y Tecnología (México

An investigation of the feasibility of applying linear progamming to assembly line balancing

An investigation of the feasibility of applying linear programming to assembly line balancing problems is presented. Salveson and Bowman developed the mathematical models

Surrogate-based modeling strategy for design optimization of passenger car suspension system

The dynamic response of a Low-Fidelity (LoFi) vehicle model exhibits a discrepancy when compared to a High-Fidelity (HiFi) vehicle model. HiFi model construction involves complex state-space equations, a high degree of freedom, and requires a huge quantity of early data to completely define this model. This causes a delay and makes the computation process less efficient. On the other hand, the LoFi model developed using simpler state-space equations is faster and computationally cheaper. However, the response accuracy of this model is lower than that of HiFi. Due to this competence mismatch, it constrains the ability and integration of LoFi model or HiFi model applications in vehicle dynamics research. In previous researches, the proposed surrogate model has been completely replaced any physics-based model for subsequent engineering applications once it has been generated. However, this model has limitation to perform fine tuning either on LoFi or HiFi models. The primary aim of this research was to formulate a surrogate-based modeling strategy by tuning LoFi model for optimizing the design of the passenger car suspension system. The study began with the development of HiFi and LoFi models in Matlab, and their performances were verified by comparing the results produced by MSC Adams software. The LoFi model was used to determine the overall relationship between the suspension system's main elements, namely spring stiffness (Ks) and damper rate (Cs), and the design criteria, namely Body Acceleration (BAcc), Dynamic Tire Load (DTL), and Suspension Workspace (SWS). Based on the Design Criteria Space (DCS) map and recommendations from the literature, the Design Objective Space (DOS) map for a passenger car suspension system was established. Following that, three approaches to formulating surrogate models were introduced, namely the Response-Based Approach (RBA), the Variable-Based Approach (VBA), and the Parameter-Based Approach (PBA). The VBA for the Quadratic Transformation Scheme (QTS) was found to be the most suitable for the proposed newly surrogate model. Next, the surrogate model was linked to an optimization strategy to tune the suspension elements. Finally, a single optimal solution was obtained using the Min-Max method. The optimal tuning for the suspension elements of the chosen passenger car was Ks = 12535.6 N/m and Cs= 1416.7 Ns/m which increased the BAcc by 12.6% but at the expense of DTL performance by 6.4%, and keeping the SWS below the 7 mm restriction. In conclusion, the proposed surrogate-based modeling strategy could be a potential tool for optimizing the design of a passenger car suspension system

A parallel genetic algorithm for the set partitioning problem

This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36, 699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints