1,720,958 research outputs found
Global Procedures for Solving Black-Box Optimization Problems
No full textSometimes it is not possible to define either the objective function or the feasible set of an Optimization Problem by means of an algebraic model. It may happen, usually if dealing with real world applications,that there is a lack of reliable analytic models for describing the system process. This can be due either to the high complexity within the system or to the fact that any kind of simplification is not allowed. In this case, the objective function (and sometimes even the feasible set) is described through a simulation process and the related Problem relies in the Simulation-Optimization framework.
The term Simulation-Optimization (SO) includes a very wide range of problems and in particular it refers to all the techniques used for optimizing stochastic simulations.
This dissertation focuses on this kind of Optimization Problems. In particular, the main topic of the whole work is Black-Box Optimization, namely the area of optimization which studies problems whose objective function is defined purely by means of an input-output model. When solving such problems, many information used by the optimization algorithms, as first order information, is not available. For this reason, over time many different optimization algorithms have been developed to tackle all the difficulties relying behind these problems structure. In the first Chapter of this dissertation are reported the main characteristics of both Simulation-Optimization Problems and Algorithms.
One of the features that differentiates between this kind of problems is the presence or not of uncertainty within the simulation model used. As exposed in Chapter \ref{BBOI}, a stochastic or deterministic simulation implies a different implementation in the structure of the algorithm to solve the related Simulation-Optimization problem.
After Chapter 1, the whole dissertation is focused on the development and the numerical experimentation of global optimization procedures able to solve Black-Box Optimization Problems starting from real world applications. In particular, Chapters 2, 3 and 4 focus on solving BB Optimization Problems with a deterministic simulation model, while in 5 a stochastic simulation model is introduced.
For each of these Chapters it has been defined the same structure: first of all, an introduction of the application is reported, including a description of the state of the art of the literature. Then, the features of the defined optimization problem are explained. Starting from these, it is possible to define an ad-hoc Optimization algorithm, able to solve the modelled problem. It follows, then, a description of the Optimization procedure design and at last some numerical results are reported. Each of them is compared with different alternative optimization algorithms, in order to define the best setting for a particular problem. Moreover, whenever it is possible, the proposed approach is compared with other proposed for a similar problem in previous works. Since we are dealing with real-world applications all the conducted analysis of the numerical results focus on both the efficiency and the effectiveness aspects for the proposed algorithms
Using SVM to combine global heuristics for the Standard Quadratic Problem
No full textThe Standard Quadratic Problem (StQP) is an NP-hard problem with many local minimizers (stationary
points). In the literature, heuristics based on unconstrained continuous non-convex formulations have been
proposed (Bomze & Palagi, 2005; Bomze, Grippo, & Palagi, 2012) but none dominates the other in terms of
best value found. Following (Cassioli, DiLorenzo, Locatelli, Schoen, & Sciandrone, 2012) we propose to use
Support Vector Machines (SVMs) to define a multistart global strategy which selects the “best” heuristic.
We test our method on StQP arising from the Maximum Clique Problem on a graph which is a challenging
combinatorial problem. We use as benchmark the clique problems in the DIMACS challenge
Bankruptcy prediction using support vector machines and feature selection during the recent financial crisis
Full text linkThis study aims at identifying an optimal set of features for predicting firms bankruptcy events in the current macroeconomic context. To this aim, among many financial features, we propose new country-specific factors which consider the macroeconomic conditions of the countries where firms operate. Our forecasting model is based on Support Vector Machines (SVMs), which are tools employed in supervised learning. Firstly, starting from a wide set of variables commonly used for bankruptcy prediction we assess the general effectiveness of SVMs also in comparison with the performances of other commonly used methods. Secondly, we try to improve the accuracy of forecasts by selecting optimal subsets of variables through a feature selection method. The results show that, in the current socio-economic context, the conjunct use of SVMs and the proposed feature selection technique significantly improves the accuracy of bankruptcy predictions compared to the performance of the other methods examined. Furthermore, we show that the proposed country-specific factors are relevant information for predicting the failure of firms and that most of the ratios proposed by Altman in 1968 are still relevant nowadays
A new tool for the evaluation of the rehabilitation outcomes in older persons. a machine learning model to predict functional status 1 year ahead
Full text linkPurpose To date, the assessment of disability in older people is obtained utilizing a Comprehensive Geriatric Assessment (CGA). However, it is often difficult to understand which areas of CGA are most predictive of the disability. The aim of this study is to evaluate the possibility to early predict—1year ahead—the disability level of a patient using machine leaning models.
Methods Community-dwelling older people were enrolled in this study. CGA was made at baseline and at 1year follow-up. After collecting input/independent variables (i.e., age, gender, schooling followed, body mass index, information on smoking, polypharmacy, functional status, cognitive performance, depression, nutritional status), we performed two distinct Support Vector Machine models (SVMs) able to predict functional status 1year ahead. To validate the choice of the model, the results achieved with the SVMs were compared with the output produced by simple linear regression models.
Results 218 patients (mean age = 78.01; SD = 7.85; male = 39%) were recruited. The combination of the two SVMs is able to achieve a higher prediction accuracy (exceeding 80% instances correctly classified vs 67% instances correctly classified by the combination of the two linear regression models). Furthermore, SVMs are able to classify both the three categories,
self sufficiently, disability risk and disability, while linear regression model separates the population only in two groups (self-sufficiency and disability) without identifying the intermediate category (disability risk) which turns out to be the most critical one.
Conclusions The development of such a model can contribute to the early detection of patients at risk of self-sufficiency loss
Europcar Integrates Forecasting, Simulation, and Optimization Techniques in a Capacity and Revenue Management System
No full textEuropcar, the leading European car rental company, partnered with ACT Operations Research to develop Opticar, an innovative decision support system. The system uses forecasting, discrete-event simulation, and optimization (solved with metaheuristic algorithms) to provide an integrated approach to revenue and capacity management. Opticar anticipates future demand for Europcar's vehicle fleet up to six months in advance, thus improving fleet capacity management (vehicle acquisitions, decommissions, and transfers). In addition, it enables Europcar to optimize its approach to revenue management and rental pricing, taking into account its currently available fleet, competitor information, and the expected demand for vehicles. Opticar provides a mathematical approach, which managers in nine countries share and use as a starting point for all daily operations
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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