León Caballero, Javier

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First Name
Last Name
León Caballero
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Estadística e Investigación Operativa
Estadística e Investigación Operativa
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Now showing 1 - 7 of 7
  • Publication
    Mathematical programming with uncertainty and multiple objectives for sustainable development and wildfire management
    (Universidad Complutense de Madrid, 2020-03-23) León Caballero, Javier; Vitoriano Villanueva, Begoña; Hearne, John
    Mathematical Programming is a well-placed field of Operational Research to tackle problems as diverse as those that arise in Logistics and Disaster Management. The fundamental objective of Mathematical Programming is the selection of an optimal alternative that meets a series of restrictions. The criterion by which the alternatives are evaluated is traditionally only one (for example, minimizing cost), however it is also common for several objectives to want to be considered simultaneously, thus giving rise to the Multi-criteria Decision. If the conditions to be met by an alternative or the evaluation of said alternative depend on random (or unknown) factors, we are in an optimization context under uncertainty. In the first chapters of this thesis the fields of multicriteria decision and optimization with uncertainty are studied, in two applications in the context of sustainable development and disaster management. Optimization with uncertainty is introduced through an application to rural electrification. In rural areas, access to electricity through solar systems installed in consumers' homes is common. These systems have to be repaired when they fail, so the decision of how to size a maintenance network is affected by great uncertainty. A mathematical programming model is developed by treating uncertainty in an unexplained way, the objective of which is to obtain a maintenance network at minimum cost. This model is later used as a tool to obtain simple rules that can predict the cost of maintenance using little information. The model is validated using information from a real program implemented in Morocco. When studying Multicriteria Optimization it is considered a problem in forest fire management. To mitigate the effects of forest fires, it is common to modify forests, with what is known as fuel treatment. Through this practice, consisting of the controlled felling or burning of trees in selected areas, it is achieved that when fires inevitably occur, they are easier to control. Unfortunately, modifying the flora can affect the existing fauna, so it is sensible to look for solutions that improve the situation in the face of a fire but without great detriment to the existing species. In other words, there are several criteria to take into account when optimizing. A mathematical programming model is developed, which suggests which zones to burn and when, taking into account these competing criteria. This model is applied to a series of simulated realistic cases. The following is a theoretical study of the field of Multiobjective Stochastic Programming (MSP), in which problems that simultaneously have various criteria and uncertainty are considered. In this chapter, a new solution concept is developed for MSP problems with risk aversion, its properties are studied and a linear programming model capable of obtaining said solution is formulated. A computational study of the model is also carried out, applying it to a variation of the well-known backpack problem. Finally, the problem of controlled burning is studied again, this time considering the existing uncertainty as it is not possible to know with certainty how many controlled burns can be carried out in a year, due to the limited window of time in which these can be carried out. The problem is solved using the multi-criteria and stochastic methodology with risk aversion developed in the previous chapter. Finally, the resulting model is applied to a real case located in southern Spain.
  • Publication
    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.
  • Publication
    A Landscape-Scale Optimisation Model to Break the Hazardous Fuel Continuum While Maintaining Habitat Quality
    (Springer, 2019) León Caballero, Javier; Reijnders, Victor M. J. J.; Hearne, John W.; Ozlen, Melih; Reinke, Karin J.
    Wildfires have demonstrated their destructive powers in several parts of the world in recent years. In an effort to mitigate the hazard of large catastrophic wildfires, a common practice is to reduce fuel loads in the landscape. This can be achieved through prescribed burning or mechanically. Prioritising areas to treat is a challenge for landscape managers. To help deal with this problem, we present a spatially explicit, multiperiod mixed integer programming model. The model is solved to yield a plan to generate a dynamic landscape mosaic that optimally fragments the hazardous fuel continuum while meeting ecosystem considerations. We demonstrate that such a multiperiod plan for fuel management is superior to a myopic strategy. We also show that a range of habitat quality values can be achieved without compromising the optimal fuel reduction objective. This suggests that fuel management plans should also strive to optimise habitat quality. We illustrate how our model can be used to achieve this even in the special case where a faunal species requires mature habitat that is also hazardous from a wildfire perspective. The challenging computational effort required to solve the model can be overcome with either a rolling horizon approach or lexicographically. Typical Australian heathland landscapes are used to illustrate the model but the approach can be implemented to prioritise treatments in any fire-prone landscape where preserving habitat connectivity is a critical constraint.
  • Publication
    Energía solar sostenible para el desarrollo rural y estimación de sus costes
    (2022) Martín-Campo, F. Javier; León Caballero, Javier; Vitoriano, Begoña; Ortuño, M. T.; Narvarte, Luis; Carrasco, Luis Miguel
    Este trabajo presenta una metodología para la estimación de costes en programas de electrificación rural descentralizada basado en la instalación y mantenimiento de sistemas solares domésticos. La metodología presentada consta de dos fases: (1) modelo de optimización para estimación de costes y configuración de la estructura local (agencias, vehículos y personal) y (2) modelos estadísticos de clasificación y regresión lineal para la estimación de costes sujeto a características conocidas de antemano en un programa de esta envergadura. Esta metodología ha sido validada en un programa implantado en Marruecos donde la empresa encargada de la instalación, gestión y operación subestimó, principalmente, los costes de operación, provocando el incumplimiento de las condiciones del programa.
  • Publication
    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.
  • Publication
    Fuel management operations planning in fire management: A bilevel optimisation approach
    (Elsevier, 2021) Liberatore, Federico; León Caballero, Javier; Hearne, John; Vitoriano, Begoña
    Elevated fuel loads represent a wildfire hazard in a landscape. Reducing fuel load is one mitigation strategy commonly employed to decrease the severity and impact of wildfires. The planning of such fuel management operations, however, represents a complicated decision problem, which includes multiple sources of uncertainty. In this paper, a problem for fuel treatment planning is presented, formulated, and solved. The optimisation model identifies the best subset of units in the landscape to be treated to minimise the impact of the worst-case wildfire. Due to its size, which would make it intractable for realistic instances, an ad hoc exact solution algorithm has been devised. Extensive computational testing on randomly generated instances illustrates that the proposed approach is very successful at solving the problem. Finally, the algorithm is applied to a case study on a landscape in Andalusia, Spain, which shows the capabilities of the proposed approach in addressing a real-world problem.
  • Publication
    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.