Consumer choice in competitive location models: Formulations and heuristics

A new direction of research in Competitive Location theory incorporates theories of Consumer Choice Behavior in its models. Following this direction, this paper studies the importance of consumer behavior with respect to distance or transportation costs in the optimality of locations obtained by traditional Competitive Location models. To do this, it considers different ways of defining a key parameter in the basic Maximum Capture model (MAXCAP). This parameter will reflect various ways of taking into account distance based on several Consumer Choice Behavior theories. The optimal locations and the deviation in demand captured when the optimal locations of the other models are used instead of the true ones, are computed for each model. A metaheuristic based on GRASP and Tabu search procedure is presented to solve all the models. Computational experience and an application to 55-node network are also presented.Distance, competitive location models, consumer choice behavior, GRASP, tabu

Race, Marriage, Markets, Choice, and Some Reflections on is Marriage for White People?

Ni thin films of 1800 Å thick were deposited by ion-plating and designed by photolithography to be used as temperature sensors. The resistive paths were finished with contact Cu welding terminals. After being coated with a protective layer of SiOx, they were subjected to heat stabilization treatments. Small, stable and accurate sensors were obtained.Se depositaron trayectos de película delgada de Ni de 1800 Á de espesor diseñados por fotolitografía para ser usados como sensores de temperatura. Los trayectos resistivos fueron provistos de contactos de Cu para soldar terminales. Después de ser recubiertos con una capa de OSi como protección, fueron sometidos a un tratamiento térmico de estabilización. Se obtuvieron sensores pequeños, estables y precisos

Handwritten digit classification

Pattern recognition is one of the major challenges in statistics framework. Its goal is the feature extraction to classify the patterns into categories. A well-known example in this field is the handwritten digit recognition where digits have to be assigned into one of the 10 classes using some classification method. Our purpose is to present alternative classification methods based on statistical techniques. We show a comparison between a multivariate and a probabilistic approach, concluding that both methods provide similar results in terms of test-error rate. Experiments are performed on the known MNIST and USPS databases in binary-level image. Then, as an additional contribution we introduce a novel method to binarize images, based on statistical concepts associated to the written trace of the digitDigit, Classification, Images

A VLT study of metal-rich extragalactic H II regions. I. Observations and empirical abundances

We have obtained spectroscopic observations from 3600 Angstrom to 9200 Angstrom with FORS at the Very Large Telescope for approximately 70 H II regions located in the spiral galaxies NGC 1232, NGC 1365, NGC 2903, NGC 2997 and NGC 5236. These data are part of a project aiming at measuring the chemical abundances and characterizing the massive stellar content of metal-rich extragalactic H II regions. In this paper we describe our dataset, and present emission line fluxes for the whole sample. In 32 H II regions we measure at least one of the following auroral lines: [S II]4072, [N II]5755, [S III]6312 and [O II]7325. From these we derive electron temperatures, as well as oxygen, nitrogen and sulphur abundances, using classical empirical methods (both so-called "Te-based methods" and "strong line methods"). Under the assumption that the temperature gradient does not introduce severe biases, we find that the most metal-rich nebulae with detected auroral lines are found at 12+log(O/H)~8.9, i.e. about 60% larger than the adopted solar value. However, classical abundance determinations in metal-rich H II regions may be severely biased and must be tested with realistic photoionization models. The spectroscopic observations presented in this paper will serve as a homogeneous and high-quality database for such purpose.Comment: Accepted for publication in Astronomy and Astrophysic

Clustering and classifying images with local and global variability

A procedure for clustering and classifying images determined by three classification variables is presented. A measure of global variability based on the singular value decomposition of the image matrices, and two average measures of local variability based on spatial correlation and spatial changes. The performance of the procedure is compared using three different databases.Images, Cluster, Classification

A new chance-constrained maximum capture location problem

The paper presents a new model based on the basic Maximum Capture model, MAXCAP. The New Chance–Constrained Maximum Capture modelintroduces a stochastic threshold constraint, which recognises the fact that a facility can be open only if a minimum level of demand is captured. A metaheuristic based on MAX–MIN ANT system and TABU search procedure is presented to solve the model. This is the first time that the MAX–MIN ANT system is adapted to solve a location problem. Computational experience and an application to 55–node network are also presented.Stochastic location, capture models