MPI parallel programming of mixed integer optimization problems using CPLEX with COIN-OR

The aim of this technical report is to present some detailed explanations in order to help to understand and use the Message Passing Interface (MPI) parallel programming for solving several mixed integer optimization problems. We have developed a C++ experimental code that uses the IBM ILOG CPLEX optimizer within the COmputational INfrastructure for Operations Research (COIN-OR) and MPI parallel computing for solving the optimization models under UNIX-like systems. The computational experience illustrates how can we solve 44 optimization problems which are asymmetric with respect to the number of integer and continuous variables and the number of constraints. We also report a comparative with the speedup and efficiency of several strategies implemented for some available number of threads.

Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.

Dynamic optimization of track components to minimize rail corrugation

In this paper, the influence on corrugation of the most significant track parameters has been examined. After this parametric study, the optimization of the track parameters to minimize the undulatory wear growth has been achieved. Finally, the influence of the dispersion of the track and contact parameters on corrugation growth has been studied. A method has been developed to obtain an optimal solution of the track parameters which minimizes corrugation growth, thus ensuring that this solution remains optimum despite dispersion of track parameters and wheel-rail contact uncertainties. This work is based on the computer application RACING (RAil Corrugation INitiation and Growth) which has been developed by the authors to predict rail corrugation features.

Generating cluster submodels from a multistage stochastic mixed integer optimization model using break stage

We present a scheme to generate clusters submodels with stage ordering from a (symmetric or a nonsymmetric one) multistage stochastic mixed integer optimization model using break stage. We consider a stochastic model in compact representation and MPS format with a known scenario tree. The cluster submodels are built by storing first the 0-1 the variables, stage by stage, and then the continuous ones, also stage by stage. A C++ experimental code has been implemented for reordering the stochastic model as well as the cluster decomposition after the relaxation of the non-anticipativiy constraints until the so-called breakstage. The computational experience shows better performance of the stage ordering in terms of elapsed time in a randomly generated testbed of multistage stochastic mixed integer problems.

Assessment of phytostabilization of Pb/Zn mine soils with organic amendments and a native metallicolous ecotype of Festuca Rubra l.: implications for amendment selection and recovery of soil health

La contaminación del suelo es una de las principales amenazas para los ecosistemas y la salud humana. Actualmente, desde un punto de vista tanto económico como ambiental, la fitoestabilización es la mejor tecnología para remediar suelos contaminados con elevadas concentraciones de metales como son los suelos mineros. La fitoestabilización asistida consiste en el empleo de plantas y enmiendas orgánicas y/o inorgánicas con el fin de reducir la movilidad y la biodisponibilidad de los contaminantes y recuperar la salud de suelo. En este trabajo se han realizado ensayos en microcosmos y en campo centrándonos en la salud del suelo minero contaminado con Pb y Zn durante un proceso de fitoestabilización empleando enmiendas orgánicas (purines vacunos, gallinaza, estiércol de oveja y lodos de papelera mezclados con gallinaza) y/o la especie metalífera Festuca rubra con el objetivo de (i) estudiar las interacciones suelo-enmienda responsables de los cambios inducidos por el proceso de quimioestabilización en las propiedades físicoquímicas y biológicas del suelo, (ii) evaluar la efectividad del proceso de fitoestabilización sobre suelos vegetados y de la revegetación sobre suelos desnudos (iii) valorar la idoneidad de distintos indicadores químicos y biológicos (parámetros microbianos y de la vegetación) para monitorizar la efectividad de la fitoestabilización asistida en términos de reducción de la biodisponibilidad de metales en el suelo, mejora de la vegetación y de la recuperación de la salud del suelo. La aplicación de enmiendas al suelo minero supone una entrada de materia orgánica y nutrientes que conduce a una disminución de la biodisponibilidad de metales, facilitando la colonización de las plantas y el crecimiento de la vegetación nativa, además de estimular la actividad microbiana del suelo. El pH del suelo es un factor crítico que condiciona la movilidad de los metales y la toxicidad del suelo. Las poblaciones microbianas de las enmiendas no modificaron la diversidad funcional de las comunidades microbianas nativas de la mina. Los purines vacunos y los lodos de papelera mezclados con gallinaza son los tratamientos más efectivos en el proceso de fitoestabilización asistida bajo condiciones de campo. La gallinaza fue el tratamiento que más estimuló el crecimiento de la vegetación nativa y la colonización en los suelos desnudos. El bioensayo de elongación radical de lechuga es un test sensible, sencillo y barato para evaluar la biodisponibilidad de metal y la ecotoxicidad del suelo. Los tocoferoles son biomarcadores de exposición a metales con potencial para su implementación en bioensayos de toxicidad. Este trabajo permite concluir que la población metalífera de F. rubra, combinada con enmiendas orgánicas, es una excelente candidata para los proyectos de fitoestabilización asistida. Además, la monitorización simultánea de los parámetros fisicoquímicos y microbiológicos del suelo y de su ecotoxicidad permite una evaluación adecuada de la salud del suelo, así como la selección de enmiendas apropiadas para el desarrollo de un proceso fitoestabilizador.

Colonization and development of Oribatida mite communities (Acari: Oribatida) in the forest reclaimed limestone mine dumps

In order to study the colonization and development of moss mites (Oribatida) communities in a Scots pine forest of a reclaimed limestone mine dump in Northern Poland, 3 plots from the dump were chosen. The selected plots differed in age, 5 years old, 35 and 50 years old. From a total of 30 samples 499 mites (Acari) were extracted in Tullgren funnel from which 262 were Oribatida. Abundance (N) was analyzed in all mites and after determining the species of both, juvenile and adult stages of oribatids, the following indices were analyzed: Abundance (N), Dominance (D), Species diversity (S), Species richness (s) and Shannon’s diversity index (H). Regarding to the results obtained; oribatid mites were dominant with the highest abundance in all assemblages (Plot 1: 139 Oribatida /299 Acari. Plot 2: 40/55 and Plot 3: 83/145). Tectocepheus velatus showed a very high dominance (45,99%) in plot 1; the highest value for Shannon’s diversity index belonged to plot 3. On the other hand, juvenile’s percentage was significantly higher than adult’s percentage, especially at plot 2 (95,02%). These results made us to conclude that the high abundance of oribatids in the youngest forest is due to T. velatus’s high abundance and that plot 3 is the best habitat for mites. Finally, the high occurrence of juvenile stages requires keeping on studying the area.

Design,optimization and in vivo evaluation of non-viral vectors for gene therapy. Applications in ocular disorders

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Framework for the Shape Optimization of Aerodynamic Profiles Using Genetic Algorithms

This study developed a framework for the shape optimization of aerodynamics profiles using computational fluid dynamics (CFD) and genetic algorithms. Agenetic algorithm code and a commercial CFD code were integrated to develop a CFD shape optimization tool. The results obtained demonstrated the effectiveness of the developed tool. The shape optimization of airfoils was studied using different strategies to demonstrate the capacity of this tool with different GA parameter combinations.

Optimization of solution-processed oligothiophene:fullerene based organic solar cells by using solvent additives

The optimization of solution-processed organic bulk-heterojunction solar cells with the acceptor-substituted quinquethiophene DCV5T-Bu-4 as donor in conjunction with PC61BM as acceptor is described. Power conversion efficiencies up to 3.0% and external quantum efficiencies up to 40% were obtained through the use of 1-chloronaphthalene as solvent additive in the fabrication of the photovoltaic devices. Furthermore, atomic force microscopy investigations of the photoactive layer gave insight into the distribution of donor and acceptor within the blend. The unique combination of solubility and thermal stability of DCV5T-Bu-4 also allows for fabrication of organic solar cells by vacuum deposition. Thus, we were able to perform a rare comparison of the device characteristics of the solution-processed DCV5T-Bu-4:PC61BM solar cell with its vacuum-processed DCV5T-Bu-4:C-60 counterpart. Interestingly in this case, the efficiencies of the small-molecule organic solar cells prepared by using solution techniques are approaching those fabricated by using vacuum technology. This result is significant as vacuum-processed devices typically display much better performances in photovoltaic cells. Keywords

Optimization of Organic-Inorganic Inter-phase in Hybrid Systems Based on Waterborne Polyurethanes and Silica Nanostructures

215 p.

On parallel computing for stochastic optimization models and algorithms

167 p.

Optimization of the pyrolysis process for the production of a biomass derived reducing agent and hydrogen-rich gases

221 p.

Instances of combinatorial optimization problems: complexity and generation

138 p.

Geomicrobiology of meromictic, metal-mine pit lakes in the Iberian Pyrite Belt and biotechnological applications

220 p.

Analysis, optimization and development of an answer scoring system

The main contribution of this work is to analyze and describe the state of the art performance as regards answer scoring systems from the SemEval- 2013 task, as well as to continue with the development of an answer scoring system (EHU-ALM) developed in the University of the Basque Country. On the overall this master thesis focuses on finding any possible configuration that lets improve the results in the SemEval dataset by using attribute engineering techniques in order to find optimal feature subsets, along with trying different hierarchical configurations in order to analyze its performance against the traditional one versus all approach. Altogether, throughout the work we propose two alternative strategies: on the one hand, to improve the EHU-ALM system without changing the architecture, and, on the other hand, to improve the system adapting it to an hierarchical con- figuration. To build such new models we describe and use distinct attribute engineering, data preprocessing, and machine learning techniques.