Docta Complutense :: Browsing by Author "Vitoriano, Begoña"

Browsing by Author "Vitoriano, Begoña"

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A Computational Definition of Aggregation Rules.
(IEEE, 2010) Rodríguez, Juan Tinguaro; López, V.; Gómez, D.; Vitoriano, Begoña; Montero, Javier
The currently-in-use definition of aggregation function is analyzed in this paper, noting that the introduced variability in the dimension of information does not avoid some obvious dysfunctions. In particular, a potential abuse of the mathematical formalism underlies such a definition, which could lead to solve a complex concept by means of a formal mathematical expression. In this paper we propose an alternative definition making emphasis on the practical implementation of aggregation functions, taking into account the objectives and limitations observed in the application of aggregation functions within the fuzzy context.
A decision support tool for humanitarian operations in natural disaster relief
(World Scientific, 2008) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier; Omaña, Antonio; Ruan, Da; Montero, Javier
In this paper we present a decision support system for primary action of international organizations devoted to natural disaster relief In particular, we pretend to build up an expert system that taking into account past experiences will help decision makers, mainly non-governmental organizations, to start or not an operation, depending on the place and the very first information about a possible natural disaster. The relevance of this issue is extreme, since such a decision must be taken as soon as possible.
A disaster-severity assessment DSS comparative analysis
(Springer, 2011) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier; Kecman, Vojislav
This paper aims to provide a comparative analysis of fuzzy rule-based systems and some standard statistical and other machine learning techniques in the context of the development of a decision support system (DSS) for the assessment of the severity of natural disasters. This DSS, whichwill be referred to as SEDD, has been proposed by the authors to help decision makers inside those Non-Governmental Organizations (NGOs) concerned with the design and implementation of international operations of humanitarian response to disasters. SEDD enables a relatively highly accurate and interpretable assessment on the consequences of almost every potential disaster scenario to be obtained through a set of easily accessible information about that disaster scenario and historical data about similar ones. Thus, although SEDD’s methodology is rather sophisticated, its data requirements are small, which, therefore, enables its use in the context of NGOs and countries requiring humanitarian aid. In this sense, SEDD opposes to some current tools which focuses on one phenomena-one place disaster scenarios (earthquakes in California, hurricanes in Florida, etc.) and/or have extensive and/or technologically sophisticated data requirements (real-time remote sensing information, exhaustive building census, etc.).Moreover, although focused on disaster response, SEDD can also be useful in other phases of disaster management, as disaster mitigation or preparedness. Particularly, the predictive accuracy and interpretability of SEDD fuzzy methodology is compared here in a disaster severity assessment context with those of multiple linear regression, linear discriminant analysis, classification trees and support vector machines. After an extensive validation over the EM-DAT disaster database, it is concluded that SEDD outperforms the methods above in the task of simultaneously providing an accurate and interpretable inference tool for the evaluation of the consequences of disasters.
A general methodology for data-based rule building and its application to natural disaster management.
(Pergamon-Elsevier Science Ltd, 2012) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier
Risks derived from natural disasters have a deeper impact than the sole damage suffered by the affected zone and its population. Because disasters can affect geostrategic stability and international safety, developed countries invest a huge amount of funds to manage these risks. A large portion of these funds are channeled through United Nations agencies and international non-governmental organizations (NGOs), which at the same time are carrying out more and more complex operations. For these reasons, technological support for these actors is required, all the more so because the global economic crisis is placing emphasis on the need for efficiency and transparency in the management of (relatively limited) funds. Nevertheless, currently available sophisticated tools for disaster management do not fit well into these contexts because their infrastructure requirements usually exceed the capabilities of such organizations. In this paper, a general methodology for inductive rule building is described and applied to natural-disaster management. The application is a data-based, two-level knowledge decision support system (DSS) prototype which provides damage assessment for multiple disaster scenarios to support humanitarian NGOs involved in response to natural disasters. A validation process is carried out to measure the accuracy of both the methodology and the DSS
A goal programming approach for farm planning with resources dimensionality
(Springer, 2011-10) Ortuño, M. T.; Vitoriano, Begoña
Crop production entails many decision making processes aimed at improving productivity and achieving the best yield from scarce resources, which are normally limited. Assuming that there is a certain technical path of tasks to be carried out within a period, and that each task can be done in different ways, the problem addressed in this paper consists of choosing how and when to carry out each one, in such a way that the tasks are scheduled in sequence at the lowest possible cost, taking account of any relations of precedence among them, and in such a way that each task is done within its time window and with the resources being assigned in a feasible way. Time windows are usually defined in a strict way, and thus, if adjusted, can be a cause of infeasibility for some of the problems, implying the need to acquire new resources. Nevertheless, in most cases small deviations from time windows could be acceptable. In this paper a mixed 0-1 programming model to attain the proposed objective is proposed, applying goal programming to model time windows in a more flexible way.
A goal programming model for humanitarian aid distribution
(World Scientific, 2008) Ortuño, M. T.; Vitoriano, Begoña; Ruiz-Rivas, A. F.; Ruan, D.; Montero, Javier; Lu, J.; Martínez, L.; DHondt, P.; Kerre, EE.
In recent years, natural and man-made disasters have been affecting increasing numbers of people throughout the world. Organisations for emergency and humanitarian aid have experienced an important growth, and efficiency in management becomes crucial. There is a lack of specific tools devoted to logistics of this special kind of interventions in developing countries, demanded by the organisations. A goal programming model that sustains a decision support system currently in development is presented, focusing on the transport problem to distribute humanitarian aid to the affected population of a disaster in a developing country.
A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics
(Pergamon-Elsevier Science Ltd, 2014-02) Liberatore, F.; Ortuño, M. T.; Tirado Domínguez, Gregorio; Vitoriano, Begoña; Scaparra, M. P.
The distribution of emergency goods to a population affected by a disaster is one of the most fundamental operations in Humanitarian Logistics. In the case of a particularly disruptive event, parts of the distribution infrastructure (e.g., bridges, roads) can be damaged. This damage would make it impossible and/or unsafe for the vehicles to reach all the centers of demand (e.g., towns and villages). In this paper, we propose and solve the problem of planning for recovery of damaged elements of the distribution network, so that the consequent distribution planning would benefit the most. We apply the model, called RecHADS, to a case study based on the 2010 Haiti earthquake. We also show empirically the importance of coordinating recovery and distribution operations optimization.
A lexicographical dynamic flow model for relief operations
(Atlantis Press, 2013-01) Tirado Domínguez, Gregorio; Martín Campo, Javier; Vitoriano, Begoña; Ortuño, M. T.
Emergency management is a highly relevant area of interest in operations research. Currently the area is undergoing widespread development. Furthermore, recent disasters have highlighted the importance of disaster management, in order to alleviate the suffering of vulnerable people and save lives. In this context, the problem of designing plans for the distribution of humanitarian aid according to the preferences of the decision maker is crucial. In this paper, a lexicographical dynamic flow model to solve this problem is presented, extending a previously introduced static flow model. The new model is validated in a realistic case study and a computational study is performed to compare both models, showing how they can be coordinated to improve their overall performance.
A lexicographical goal programming based decision support system for logistics of Humanitarian Aid
(Springer, 2011-12) Ortuño, M. T.; Tirado Domínguez, Gregorio; Vitoriano, Begoña
Each year people affected by disasters, either natural or human-made, can be counted by millions. When a major disaster strikes a country, local and international communities usually respond with an outpouring of assistance, which has to be efficiently managed in order to arrive where it is needed as soon as possible and under adverse conditions. Despite its importance, not until recently has Humanitarian Logistics received much attention as a specific field, and there is a lack of specific tools. In this work, a lexicographical goal programming model for distribution of goods to the affected population of a disaster in a developing country is presented, which sustains a decision support system currently in development.
A Mathematical Pre-Disaster Model with Uncertainty and Multiple Criteria for Facility Location and Network Fortification
(MDPI, 2020-04-03) Monzón, Julia; Liberatore, Federico; Vitoriano, Begoña
Disasters have catastrophic effects on the affected population, especially in developing and underdeveloped countries. Humanitarian Logistics models can help decision-makers to efficiently and effectively warehouse and distribute emergency goods to the affected population, to reduce casualties and suffering. However, poor planning and structural damage to the transportation infrastructure could hamper these efforts and, eventually, make it impossible to reach all the affected demand centers. In this paper, a pre-disaster Humanitarian Logistics model is presented that jointly optimizes the prepositioning of aid distribution centers and the strengthening of road sections to ensure that as much affected population as possible can efficiently get help. The model is stochastic in nature and considers that the demand in the centers affected by the disaster and the state of the transportation network are random. Uncertainty is represented through scenarios representing possible disasters. The methodology is applied to a real-world case study based on the 2018 storm system that hit the Nampula Province in Mozambique.
A methodology for building fuzzy rules from data
(Public University of Navarre, 2009) Rodríguez, Juan Tinguaro; Lopez, Victoria; Montero, Javier; Vitoriano, Begoña; Burillo, P.; Bustince, H.; De Baets, B.; Fodor, J.
Extraction of rules for classification and decision tasks from databases is an issue of growing importance as automated processes based on data are being required in these fields. Interpretability of rules is improved by defining classes for independent variables. Moreover, though more complex, a more realistic and flexible framework is attained when fuzzy classes are considered. In this paper, an inductive approach is taken in order to develop a general methodology for building fuzzy rules from databases. Three types of rules are built in order to be able of dealing with both categorical and numerical data.
A methodology for designing electrification programs for remote areas
(Springer, 2019-09-21) León Caballero, Javier; Martín-Campo, F. Javier; Ortuño, M. T.; Vitoriano, Begoña; Carrasco, Luis Miguel; Narvarte, Luis
One of the UN Sustainable Development Goals is the supply of sustainable energy even where no electrical grid is available. The photovoltaic rural electrification programs are the most common systems implemented in remote areas, especially in developing countries. These programs include the systems installation and their maintenance for a given period. Installation costs and even spare parts costs over time are usually well estimated. However, design and cost estimation of the maintenance systems is a difficult task, whose wrong management has often resulted in the failure of these electrification programs. In this work, a methodology for designing maintenance systemsand estimating costs is presented. The methodology includes a mixed integer linear programming model and a rule based expert system. The mathematical programming model allows obtaining the optimal size and accurate cost estimation of a maintenance system, based on precise information about the installed systems. This model is calibrated and validated with real running programs and will be used to get an enlarged data set of simulated cases if needed. The rule based expert system is obtained from the data set applying classification and regression methods with general information about the region and program to be run. It can be used for designing programs or for companies making decisions about being involved in a program to be developed. The methodology has been applied to real Morocco programs.
A multi-criteria optimization model for humanitarian aid distribution
(Springer, 2011) Vitoriano, Begoña; Ortuño, M. T.; Tirado Domínguez, Gregorio; Montero, Javier
Natural disasters are phenomenons which strike countries all around the world. Sometimes, either by the intensity of the phenomenon or the vulnerability of the country, help is requested from the rest of the world and relief organizations respond by delivering basic aid to those in need. Humanitarian logistics is a critical factor in managing relief operations and, in general, there is a lack of attention on the development of mathematical models and solution algorithms for strategic and tactical decisions in this area. We acknowledge that in humanitarian logistics traditional costminimizingmeasures are not central, and postulate that other performance measures such as time of response, equity of the distribution or reliability and security of the operation routes become more relevant. In this paper several criteria for an aid distribution problem are proposed and a multi-criteria optimization model dealing with all these aspects is developed. This model is the core of a decision support system under development to assist organizations in charge of the distribution of humanitarian aid. Once the proposed criteria and the model are described, an illustrative case study based on the 2010 Haiti catastrophic earthquake is presented, showing the usefulness of the proposal.
A natural-disaster management DSS for Humanitarian Non-Governmental Organisations
(Elservier, 2010) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier
Humanitarian Non-Governmental Organisations (NGOs) play a growing role in the response to natural disasters, but despite being largely demanded, there is no available decision support system (DSS) specifically designed to address their problem. In this paper we present a decision support system (DSS) to aid those Humanitarian NGOs concerned with the response to natural disasters. Such a DSS has been designed avoiding sophisticated methodologies that may exceed the infrastructural requirements and constraints of emergency management by NGOs. A data-based, two-level knowledge methodology which allows damage assessment of multiple disaster scenarios is presented in order to address that problem. Validation results show viability of our approach.
A risk-averse solution for the prescribed burning problem
(Elsevier Science, 2023-11-02) León Caballero, Javier; Vitoriano, Begoña; Hearne, John
Hazard reduction is a complex task involving important efforts to prevent and mitigate the consequences of disasters. Many countries around the world have experienced devastating wildfires in recent decades and risk reduction strategies are now more important than ever. Reducing contiguous areas of high fuel load through prescribed burning is a fuel management strategy for reducing wildfire hazard. Unfortunately, this has an impact on the habitat of fauna and thus constrains a prescribed burning schedule which is also subject to uncertainty. To address this problem a mathematical programming model is proposed for scheduling prescribed burns on treatment units on a landscape over a planning horizon. The model takes into account the uncertainty related to the conditions for performing the scheduled prescribed burns as well as several criteria related to the safety and quality of the habitat. This multiobjective stochastic problem is modelled from a riskaverse perspective whose aim is to minimize the worst achievement of the criteria on the different scenarios considered. This model is applied to a real case study in Andalusia (Spain) comparing the solutions achieved with the risk-neutral solution provided by the simple weighted aggregated average. The results obtained show that our proposed approach outperforms the risk-neutral solution in worst cases without a significant loss of quality in the global set of scenarios.
A Risk-Aversion Approach for the Multiobjective Stochastic Programming Problem
(MDPI, 2020-11-13) León Caballero, Javier; Puerto, Justo; Vitoriano, Begoña
Multiobjective stochastic programming is a field that is well suited to tackling problems that arise in many fields: energy, financial, emergencies, among others; given that uncertainty and multiple objectives are usually present in such problems. A new concept of solution is proposed in this work, which is especially designed for risk-averse solutions. The proposed concept combines the notions of conditional value-at-risk and ordered weighted averaging operator to find solutions protected against risks due to uncertainty and under-achievement of criteria. A small example is presented in order to illustrate the concept in small discrete feasible spaces. A linear programming model is also introduced to obtain the solution in continuous spaces. Finally, computational experiments are performed by applying the obtained linear programming model to the multiobjective stochastic knapsack problem, gaining insight into the behaviour of the new solution concept. g insight into the behaviour of the new solution concept.
A simulation approach to reliability analysis of weapon systems
(Elsevier Science, 1997-07) Yáñez, Javier; Ortuño, M. T.; Vitoriano, Begoña
We report a modeling simulation approach to analyse weapon systems reliability. The introduced functional diagram generalises the logic diagram allowing the replication on the functioning mode of system components. To handle the functional diagram, the availability and connection rules are also introduced. Based on the functional diagram, a simulation model is outlined and a case study, the propulsion system of a Mine Hunter, is included.
An axiomatic approach to the notion of semantic antagonism
(Society for soft computing, 2011) Rodríguez, Juan Tinguaro; Franco, Camilo; Vitoriano, Begoña; Montero, Javier; Hiroto, K.; Mukaidono, M.; Kuswadi, S.
The concept of semantic antagonism refers to the human capability of characterizing those objects of an universe of discourse being dissimilar, significantly different from or opposite to a given concept, predicate or previous knowledge. This capability is essential in the formation of linguistic polarities, such as false/true or good/bad, that enable us to analyze, organize (classify) and give meaning to reality in terms of opposite poles of semantic reference. Though they are related, the notion of semantic antagonism is somehow more general than that of antonymy, since the former allows to characterize opposition even in the absence of antonym words and is not constrained by the assumption of symmetry that underlies the last. Therefore, the notion of semantic antagonism seems to be well suited for giving base to those knowledge representation frameworks which introduce some kind of bipolarity or distinction between positive and negative information. In this paper, an axiomatic approach is taken in order to describe the reasonable assumptions a dissimilarity operator acting on a set of predicates should obey. This enables to derive a basic differentiation of these operators, and particularly to show that antonyms are special cases of antagonistic predicates. Furthermore, through the proposed axioms it is possible to introduce the idea of dissimilarity structure over a set of predicates, and the application of this last notion in the context of supervised learning for classification tasks is briefly described.
An Inductive Methodology for Data-Based Rules Building
(Springer-Verlag Berlin Heidelberg, 2009) Rodríguez, Juan Tinguaro; Montero, Javier; Vitoriano, Begoña; Lopez, Victoria; Rossi, Francesca; Tsoukias, Alexis
Extraction of rules from databases for classification and decision tasks is an issue of growing importance as automated processes based on data are being required in these fields. An inductive methodology for data-based rules building and automated learning is presented in this paper. A fuzzy framework is used for knowledge representation and, through the introduction and the use of dual properties in the valuation space of response variables, reasons for and against the rules are evaluated from data. This make possible to use continuous DDT logic, which provides a more general and informative framework, in order to assess the validity of rules and build an appropriate knowledge base.
Clasificación borrosa basada en disimilitud para la valoración inicial de desastres
(Desconocido, 2013) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier; Alonso-Betanzos, Amparo; Bielza, Concha; Salmerón, Antonio; Duarte, Abraham; Hidalgo, J. Ignacio; Martínez, Luis; Barrenechea, Edurne; Troncoso, Alicia; Corchado, Emilio; Corchado, Juan M.; Herrera, Francisco; Riquelme, José C.
En la gestión de desastres y emergencias, es crucial una valoración inicial correcta de las consecuencias de los fenómenos adversos para permitir una toma de decisiones adecuada. Sin embargo, no se requiere que esta valoración inicial sea necesariamente totalmente precisa, por lo que su obtención puede asimilarse con un problema de clasificación borrosa en el que las clases presentan una estructura relevante, que emana de la semántica del contexto y de los requisitos del problema de decisión. Este trabajo propone la consideración de un operador de disimilitud para la introducción de esta estructura en los procesos de aprendizaje y razonamiento de un clasificador borroso, lo que redunda en una mejora de la adaptación del clasificador a las características y los requisitos en términos de toma de decisiones del contexto de la gestión de desastres.

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