Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Estimación de la concentración de material particulado mediante sensoramiento remoto en la provincia de Lima, 2020

La contaminación del aire es una de las mayores preocupaciones, ya que, genera afectaciones en la salud y el ambiente, por otro lado, el monitoreo mediante estaciones convencionales tiene un alto costo y requiere constante mantenimiento generando brechas temporales a largo plazo. En tal sentido, la provincia de Lima por su gran expansión urbana tiene una alta contaminación por material particulado y las estaciones actuales tienen desventajas. Es por ello, que el objetivo de esta investigación fue estimar la concentración de material particulado mediante sensoramiento remoto en la provincia de Lima. Para ello, se utilizaron las imágenes multiespectrales del sensor MSI a bordo de los satélites Sentinel 2A y 2B, por otro lado, se solicitaron a las estaciones automáticas de SENAMHI los datos de material particulado (PM10 y PM2.5) a escala diaria y horaria para el periodo conformado por los años 2017 al 2020. Las imágenes multiespectrales se dividieron según el porcentaje de nubosidad (20% <= NUBOSIDAD < 20%), así mismo, se calculó la reflectancia en la parte superior de la atmosfera (TOA). De esta manera, en función a los datos de material particulado solicitados se identificaron las bandas espectrales que influyeron significativamente en la estimación de estos contaminantes, adicionalmente, mediante el análisis de varianza se validaron las ecuaciones obtenidas (p-valor < 0.05), finalmente al contrastar los valores medidos con los estimados se obtuvo como resultado que el poder estimador para las concentraciones de PM10 a escala diaria fueron mayores con coeficientes de determinación de 0.63 (20% <= NUBOSIDAD) y de 0.65 (NUBOSIDAD < 20%), para el caso de las concentraciones horarias se obtuvieron coeficientes de determinación de 0.52 (20% <= NUBOSIDAD) y 0.35 (NUBOSIDAD < 20%). En el caso de las concentraciones de PM2.5 el poder estimador fue mínimo, puesto que, se obtuvieron valores de 0.41 (20% <= NUBOSIDAD) y 0.45 (NUBOSIDAD < 20%) a escala diaria y de 0.30 (20% <= NUBOSIDAD) y 0.34 (NUBOSIDAD < 20%) a escala horaria

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods