161 research outputs found

    A mathematical programming approach for dispatching and relocating EMS vehicles.

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    We consider the problem of dispatching and relocating EMS vehicles during a pandemic outbreak. In such a situation, the demand for EMS vehicles increases and in order to better utilize their capacity, the idea of serving more than one patient by an ambulance is introduced. Vehicles transporting high priority patients cannot serve any other patient, but those transporting low priority patients are allowed to be rerouted to serve a second patient. We have considered three separate problems in this research. In the first problem, an integrated model is developed for dispatching and relocating EMS vehicles, where dispatchers determine hospitals for patients. The second problem considers just relocating EMS vehicles. In the third problem only dispatching decisions are made where hospitals are pre-specified by patients not by dispatchers. In the first problem, the objective is to minimize the total travel distance and the penalty of not meeting specific constraints. In order to better utilize the capacity of ambulances, we allow each ambulance to serve a maximum of two patients. Considerations are given to features such as meeting the required response time window for patients, batching non-critical and critical patients when necessary, ensuring balanced coverage for all census tracts. Three models are proposed- two of them are linear integer programing and the other is a non-linear programing model. Numerical examples show that the linear models can be solved using general-purpose solvers efficiently for large sized problems, and thus it is suitable for use in a real time decision support system. In the second problem, the goal is to maximize the coverage for serving future calls in a required time window. A linear programming model is developed for this problem. The objective is to maximize the number of census tracts with single and double coverage, (each with their own weights) and to minimize the travel time for relocating. In order to tune the parameters in this objective function, an event based simulation model is developed to study the movement of vehicles and incidents (911 calls) through a city. The results show that the proposed model can effectively increase the system-wide coverage by EMS vehicles even if we assume that vehicles cannot respond to any incidents while traveling between stations. In addition, the results suggest that the proposed model outperforms one of the well-known real time repositioning models (Gendreau et al. (2001)). In the third problem, the objective is to minimize the total travel distance experienced by all EMS vehicles, while satisfying two types of time window constraints. One requires the EMS vehicle to arrive at the patients\u27 scene within a pre-specified time, the other requires the EMS vehicle to transport patients to their hospitals within a given time window. Similar to the first problem, each vehicle can transport maximum two patients. A mixed integer program (MIP) model is developed for the EMS dispatching problem. The problem is proved to be NP-hard, and a simulated annealing (SA) method is developed for its efficient solution. Additionally, to obtain lower bound, a column generation method is developed. Our numerical results show that the proposed SA provides high quality solutions whose objective is close to the obtained lower bound with much less CPU time. Thus, the SA method is suitable for implementation in a real-time decision support system

    Advances and applications in high-dimensional heuristic optimization

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    “Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem. These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv

    Image understanding and feature extraction for applications in industry and mapping

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    Bibliography: p. 212-220.The aim of digital photogrammetry is the automated extraction and classification of the three dimensional information of a scene from a number of images. Existing photogrammetric systems are semi-automatic requiring manual editing and control, and have very limited domains of application so that image understanding capabilities are left to the user. Among the most important steps in a fully integrated system are the extraction of features suitable for matching, the establishment of the correspondence between matching points and object classification. The following study attempts to explore the applicability of pattern recognition concepts in conjunction with existing area-based methods, feature-based techniques and other approaches used in computer vision in order to increase the level of automation and as a general alternative and addition to existing methods. As an illustration of the pattern recognition approach examples of industrial applications are given. The underlying method is then extended to the identification of objects in aerial images of urban scenes and to the location of targets in close-range photogrammetric applications. Various moment-based techniques are considered as pattern classifiers including geometric invariant moments, Legendre moments, Zernike moments and pseudo-Zernike moments. Two-dimensional Fourier transforms are also considered as pattern classifiers. The suitability of these techniques is assessed. These are then applied as object locators and as feature extractors or interest operators. Additionally the use of fractal dimension to segment natural scenes for regional classification in order to limit the search space for particular objects is considered. The pattern recognition techniques require considerable preprocessing of images. The various image processing techniques required are explained where needed. Extracted feature points are matched using relaxation based techniques in conjunction with area-based methods to 'obtain subpixel accuracy. A subpixel pattern recognition based method is also proposed and an investigation into improved area-based subpixel matching methods is undertaken. An algorithm for determining relative orientation parameters incorporating the epipolar line constraint is investigated and compared with a standard relative orientation algorithm. In conclusion a basic system that can be automated based on some novel techniques in conjunction with existing methods is described and implemented in a mapping application. This system could be largely automated with suitably powerful computers

    Evolving artificial terrains with automated genetic terrain programing

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    La industria del videojuego afronta en la actualidad un gran reto: mantener el coste del desarrollo de los proyectos bajo control a medida que estos crecen y se hacen más complejos. La creación de los contenidos de los juegos, que incluye el modelado de personajes, mapas y niveles, texturas, efectos sonoros, etc, representa una parte fundamental del costo final de producción. Por eso, la industria está cada vez más interesada en la utilización de métodos procedurales de generación automática de contenidos. Sin embargo, crear y afinar los métodos procedurales no es una tarea trivial. En esta memoria, se describe un método procedural basado en Programación Genética, que permite la generación automática de terrenos para videojuegos. Los terrenos presentan características estéticas, y no requieren ningún tipo de parametrización para definir su aspecto. Así, el ahorro de tiempo y la reducción de costes en el proceso de producción es notable. Para conseguir los objetivos, se utiliza Programación Genética de Terrenos. La primera implementación de GTP utilizó Evolución Interactiva, en que la presencia del usuario que guía el proceso evolutivo es imprescindible. A pesar de los buenos resultados, el método está limitado por la fatiga del usuario (común en los métodos interactivos). Para resolver esta cuestión se desarrolla un nuevo modelo de GTP en el que el proceso de búsqueda es completamente automático, y dirigido por una función de aptitudo. La función considera accesibilidad de los terrenos y perímetros de los obstáculos. Los resultados obtenidos se incluyeron como parte de un videojuego real.Nowadays video game industry is facing a big challenge: keep costs under control as games become bigger and more complex. Creation of game content, such as character models, maps, levels, textures, sound effects and so on, represent a big slice of total game production cost. Hence, video game industry is increasingly turning to procedural content generation to amplify the cost-effectiveness of video game designers' efforts. However, creating and fine tunning procedural methods for automated content generation is a time consuming task. In this thesis we detail a Genetic Programming based procedural content technique to generate procedural terrains. Those terrains present aesthetic appeal and do not require any parametrization to control its look. Thus, allowing to save time and help reducing production costs. To accomplish these features we devised the Genetic Terrain Programming (GTP) technique. The first implementation of GTP used an Interactive Evolutionary Computation (IEC) approach, were a user guides the evolutionary process. In spite of the good results achieved this way, this approach was limited by user fatigue (common in IEC systems). To address this issue a second version of GTP was developed where the search is automated, being guided by a direct fitness function. That function is composed by two morphological metrics: terrain accessibility and obstacle edge length. The combination of the two metrics allowed us remove the human factor form the evolutionary process and to find a wide range of aesthetic and fit terrains. Procedural terrains produced by GTP are already used in a real video game.Ministerio de Educación y Ciencia (TIN2007-68083-C02-01); (TIN2008-05941); (TIN2011-28627-C04) Junta de Extremadura (GRU-09105); (GR10029) Junta de Andalucía (TIC-6083

    GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

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    Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

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    These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)
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