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This paper presents an effectiveness strategy to model and solve nonlinear mathematical optimization problems in electrical engineering using General Algebraic Modeling System (GAMS) for undergraduate students. A classical problem known as economic dispatch has been selected to show the need of using mathematical tools to solve a large scale optimization problem related with engineering. The economic dispatch is a classical optimization problem in operation of thermal electric systems, being the main idea to find an economical operation for thermal generators. This operation is based on a quadratic cost curve with some operating constraints, i.e., power balance and generation capacities. A numerical simulation is implemented using GAMS optimization package in demo version. This research has been developed with the support of 36 students of the course of Regulation and Operation of Electrical Systems (ROES) in the program of Electrical Engineering at Universidad Tecnológica de Pereira (UTP). [ABSTRACT FROM AUTHOR]

The aim of this paper study the impact of class-based storage policy based on the optimal configuration of U-flow single command warehouses using a on an ABC product classification. For that purpose, the authors propose a non linear optimization model to minimize the expected travel distance of the warehouse and use analytical methods to solve it. The most important contribution is to provide a mathematical proof that regardless of the storage policy (It does not matter the specifics of the turnover pattern of the products), the optimal warehouse has a width that is the double of its length, and the pick and deposit point should be located in the middle of the width of the warehouse. In addition, authors perform a sensitivity analysis that indicates that the optimal solution is robust, meaning that a certain deviation from the optimum layout does not impose a significant penalty on the expected travel distance of the warehouse. [ABSTRACT FROM AUTHOR]

The purpose of the optimum designs is to determine the optimal experimental conditions in terms of minimum variance of the estimated vector of parameters, so that statistical inferences can be generated as accurate as possible. This theory presupposes knowledge of the function relating the explanatory variables with the response variable. In this situation, usually get designs with support points as parameters in the model. Since p-point designs assume that the model function is known, these could not be optimal in some practical situations because they do not allow to test the goodness of fit of the assumed model. In this paper we present a generalization of the methodology proposed by to increase the number of support points in D-optimality criterion. An expression for the variance of the predicted response in terms of a constant δ will be provided, which allow to determine the points to be added to the D-optimal design. We propose a strategy for choosing δ. Finally we provide a pseudo-optimal design with (p + s) support points. [ABSTRACT FROM AUTHOR]