The multimode covering location problem

In this paper we introduce the Multimode Covering Location Problem. This is a generalization of the Maximal Covering Location Problem that consists in locating a given number of facilities of different types with a limitation on the number of facilities sharing the same site. The problem is challenging and intrinsically much harder than its basic version. Nevertheless, it admits a constant factor approximation guarantee, which can be achieved combining two greedy algorithms. To improve the greedy solutions, we have developed a Variable Neighborhood Search approach, based on an exponential-size neighborhood. This algorithm computes good quality solutions in short computational time. The viability of the approach here proposed is also corroborated by a comparison with a Heuristic Concentration algorithm, which is presently the most effective approach to solve large instances of the Maximal Covering Location Problem

Multimode extensions of combinatorial optimization problems

We review some complexity results and present a viable heuristic approach based on the Variable Neighborhood Search (VNS) framework for multimode extension of combinatorial optimization problems, such as the the Set Covering Problem (SCP) and the Covering Location Problem (CLP)

Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling

In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems

Reactive scheduling to treat disruptive events in the MRCPSP

Esta tesis se centra en diseñar y desarrollar una metodología para abordar el MRCPSP con diversas funciones objetivo y diferentes tipos de interrupciones. En esta tesis se exploran el MRCPSP con dos funciones objetivo, a saber: (1) minimizar la duración del proyecto y (2) maximizar el valor presente neto del proyecto. Luego, se tiene en cuenta dos tipos diferentes de interrupciones, (a) interrupción de duración, e (b) interrupción de recurso renovable. Para resolver el MRCPSP, en esta tesis se proponen tres estrategias metaheurísticas: (1) algoritmo memético para minimizar la duración del proyecto, (2) algoritmo adaptativo de forrajeo bacteriano para maximizar el valor presente neto del proyecto y (3) algoritmo de optimización multiobjetivo de forrajeo bacteriano (MBFO) para resolver el MRCPSP con eventos de interrupción. Para juzgar el rendimiento del algoritmo memético y de forrajeo bacteriano propuestos, se ha llevado a cabo un extenso análisis basado en diseño factorial y diseño Taguchi para controlar y optimizar los parámetros del algoritmo. Además se han puesto a prueba resolviendo las instancias de los conjuntos más importantes en la literatura: PSPLIB (10,12,14,16,18,20 y 30 actividades) y MMLIB (50 y 100 actividades). También se ha demostrado la superioridad de los algoritmos metaheurísticos propuestos sobre otros enfoques heurísticos y metaheurísticos del estado del arte. A partir de los estudios experimentales se ha ajustado la MBFO, utilizando un caso de estudio.DoctoradoDoctor en Ingeniería Industria

Anisotropic Fast-Marching on cartesian grids using Lattice Basis Reduction

We introduce a modification of the Fast Marching Algorithm, which solves the generalized eikonal equation associated to an arbitrary continuous riemannian metric, on a two or three dimensional domain. The algorithm has a logarithmic complexity in the maximum anisotropy ratio of the riemannian metric, which allows to handle extreme anisotropies for a reduced numerical cost. We prove the consistence of the algorithm, and illustrate its efficiency by numerical experiments. The algorithm relies on the computation at each grid point of a special system of coordinates: a reduced basis of the cartesian grid, with respect to the symmetric positive definite matrix encoding the desired anisotropy at this point.Comment: 28 pages, 12 figure

Toward Smart Building Design Automation: Extensible CAD Framework for Indoor Localization Systems Deployment

Over the last years, many smart buildings applications, such as indoor localization or safety systems, have been subject of intense research. Smart environments usually rely on several hardware nodes equipped with sensors, actuators, and communication functionalities. The high level of heterogeneity and the lack of standardization across technologies make design of such environments a very challenging task, as each installation has to be designed manually and performed ad-hoc for the specific building. On the other hand, many different systems show common characteristics, like the strict dependency with the building floor plan, also sharing similar requirements such as a nodes allocation that provides sensing coverage and nodes connectivity. This paper provides a computer-aided design application for the design of smart building systems based on the installation of hardware nodes across the indoor space. The tool provides a site-specific algorithm for cost-effective deployment of wireless localization systems, with the aim to maximize the localization accuracy. Experimental results from real-world environment show that the proposed site-specific model can improve the positioning accuracy of general models from the state-of-the-art. The tool, available open-source, is modular and extensible through plug-ins allowing to model building systems with different requirements

Resilient optical multicasting utilizing cycles in WDM optical networks

High capacity telecommunications of today is possible only because of the presence of optical networks. At the heart of an optical network is an optical fiber whose data carrying capabilities are unparalleled. Multicasting is a form of communication in wavelength division multiplexed (WDM) networks that involves one source and multiple destinations. Light trees, which employ light splitting at various nodes, are used to deliver data to multiple destinations. A fiber cut has been estimated to occur, on an average, once every four days by TEN, a pan-European carrier network. This thesis presents algorithms to make multicast sessions survivable against component failures. We consider multiple link failures and node failures in this work. The two algorithms presented in this thesis use a hybrid approach which is a combination of proactive and reactive approaches to recover from failures. We introduce the novel concept of minimal-hop cycles to tolerate simultaneous multiple link failures in a multicast session. While the first algorithm deals only with multiple link failures, the second algorithm considers the case of node failure and a link failure. Two different versions of the first algorithm have been implemented to thoroughly understand its behavior. Both algorithms were studied through simulators on two different networks, the USA Longhaul network and the NSF network. The input multicast sessions to all our algorithms were generated from power efficient multicast algorithms that make sure the power in the receiving nodes are at acceptable levels. The parameters used to evaluate the performance of our algorithms include computation times, network usage and power efficiency. Two new parameters, namely, recovery times and recovery success probability, have been introduced in this work. To our knowledge, this work is the first to introduce the concept of minimal hop cycles to recover from simultaneous multiple link failures in a multicast session in optical networks

A bi-objective turning restriction design problem in urban road networks

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因子分析モデルにおける遺伝的アルゴリズムを用いたモデル探索

制度:新 ; 文部省報告番号:甲2456号 ; 学位の種類:博士(文学) ; 授与年月日:2007/6/19 ; 早大学位記番号:新456

A detector interferometric calibration experiment for high precision astrometry

Context: Exoplanet science has made staggering progress in the last two decades, due to the relentless exploration of new detection methods and refinement of existing ones. Yet astrometry offers a unique and untapped potential of discovery of habitable-zone low-mass planets around all the solar-like stars of the solar neighborhood. To fulfill this goal, astrometry must be paired with high precision calibration of the detector. Aims: We present a way to calibrate a detector for high accuracy astrometry. An experimental testbed combining an astrometric simulator and an interferometric calibration system is used to validate both the hardware needed for the calibration and the signal processing methods. The objective is an accuracy of 5e-6 pixel on the location of a Nyquist sampled polychromatic point spread function. Methods: The interferometric calibration system produced modulated Young fringes on the detector. The Young fringes were parametrized as products of time and space dependent functions, based on various pixel parameters. The minimization of func- tion parameters was done iteratively, until convergence was obtained, revealing the pixel information needed for the calibration of astrometric measurements. Results: The calibration system yielded the pixel positions to an accuracy estimated at 4e-4 pixel. After including the pixel position information, an astrometric accuracy of 6e-5 pixel was obtained, for a PSF motion over more than five pixels. In the static mode (small jitter motion of less than 1e-3 pixel), a photon noise limited precision of 3e-5 pixel was reached