Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Criticality Analysis of Activity Networks under Interval Uncertainty

Dedicated to the memory of Professor Stefan Chanas - The extended abstract version of this paper has appeared in Proceedings of 11th International Conference on Principles and Practice of Constraint Programming (CP2005) ("Interval Analysis in Scheduling", Fortin et al. 2005)International audienceThis paper reconsiders the Project Evaluation and Review Technique (PERT) scheduling problem when information about task duration is incomplete. We model uncertainty on task durations by intervals. With this problem formulation, our goal is to assert possible and necessary criticality of the different tasks and to compute their possible earliest starting dates, latest starting dates, and floats. This paper combines various results and provides a complete solution to the problem. We present the complexity results of all considered subproblems and efficient algorithms to solve them

Development of a wind gust model to estimate gust speeds and their return periods

Spatially dense observations of gust speeds are necessary for various applications, but their availability is limited in space and time. This work presents an approach to help to overcome this problem. The main objective is the generation of synthetic wind gust velocities. With this aim, theoretical wind and gust distributions are estimated from 10 yr of hourly observations collected at 123 synoptic weather stations provided by the German Weather Service. As pre-processing, an exposure correction is applied on measurements of the mean wind velocity to reduce the influence of local urban and topographic effects. The wind gust model is built as a transfer function between distribution parameters of wind and gust velocities. The aim of this procedure is to estimate the parameters of gusts at stations where only wind speed data is available. These parameters can be used to generate synthetic gusts, which can improve the accuracy of return periods at test sites with a lack of observations. The second objective is to determine return periods much longer than the nominal length of the original time series by considering extreme value statistics. Estimates for both local maximum return periods and average return periods for single historical events are provided. The comparison of maximum and average return periods shows that even storms with short average return periods may lead to local wind gusts with return periods of several decades. Despite uncertainties caused by the short length of the observational records, the method leads to consistent results, enabling a wide range of possible applications

Mixed Climatology, Non-synoptic Phenomena and Downburst Wind Loading of Structures

Modern wind engineering was born in 1961, when Davenport published a paper in which meteorology, micrometeorology, climatology, bluff-body aerodynamics and structural dynamics were embedded within a homogeneous framework of the wind loading of structures called today \u201cDavenport chain\u201d. Idealizing the wind with a synoptic extra-tropical cyclone, this model was so simple and elegant as to become a sort of axiom. Between 1976 and 1977 Gomes and Vickery separated thunderstorm from non-thunderstorm winds, determined their disjoint extreme distributions and derived a mixed model later extended to other Aeolian phenomena; this study, which represents a milestone in mixed climatology, proved the impossibility of labelling a heterogeneous range of events by the generic term \u201cwind\u201d. This paper provides an overview of this matter, with particular regard to the studies conducted at the University of Genova on thunderstorm downbursts

Large-Diameter Single-Wall Carbon Nanotubes Formed Alongside Small-Diameter Double-Walled Carbon Nanotubes

Samples containing a majority of either single-wall carbon nanotubes (SWCNTs) or double-walled carbon nanotubes (DWCNTs) are prepared in the same catalytic chemical vapor deposition conditions but using slightly different catalytic materials, based on alumina impregnated with iron and molybdenum salts. There is a sharp SWCNTs-to-DWCNTs transition. By contrast to the usual findings, the selectivity is not correlated to the size of the iron-based catalyst nanoparticles, nor does the transition occur upon a decreasing carbon/catalyst ratio. The result is attributed to the increasing MoO3 concentration inducing modifications of the gas atmosphere, such as the formation of more reactive C2 species through C2H4 dissociation, which thus favors the nucleation and growth of a DWCNT. In the DWCNT sample, the average diameter of the SWCNTs is higher than the average outer diameter of the DWCNTs, which is uncommon, as many authors stress that SWCNTs show a lower diameter than DWCNTs. The study could provide guidelines for the synthesis of very small diameter DWCNTs