Shaper-GA: automatic shape generation for modular housing

This work presents an automatic system that, from the specification of an architectural language of design, generates several alternative floor plants for the construction of modular homes. The system uses Genetic Algorithms and is capable of efficiently producing various plant solutions. The rules of architecture are implemented in the fitness function translating the rules of a Shape Grammar created by the architect. Different solutions of feasible plants are generated, that is, solutions that obey the rules of Shape Grammar and do not have overlays between the rooms. The system can be integrated with a user-friendly interface in the future, to allow for the house owners customization of their own house. Such a tool can also be delivered to construction companies for them to manage the design of modular houses that meet specific clients requirements.Este trabalho apresenta um sistema automático que, a partir da especificação de uma linguagem arquitetural de design, gera plantas alternativas para residências de construção modular. O sistema usa Algoritmos Genéticos e é capaz de produzir várias soluções de plantas de modo eficiente. As regras de arquitetura são implementadas na função de fitness a partir de uma Gramática de Forma criada pelo arquiteto. São geradas diferentes soluções de plantas exequíveis, isto é, soluções que obedecem à Gramática de Forma e não têm sobreposições entre as suas divisões. Pode ser futuramente integrado com uma interface amigável para o utilizador de forma a que este personalize e crie a sua futura casa. Tal ferramenta pode também ser entregue às companhias de construção de forma a que estas gerem uma planta para uma casa modular personalizada

Problemas de corte: métodos exactos y aproximados para formulaciones mono y multi-objetivo

Los problemas de corte y empaquetado son una familia de problemas de optimización combinatoria que han sido ampliamente estudiados en numerosas áreas de la industria y la investigación, debido a su relevancia en una enorme variedad de aplicaciones reales. Son problemas que surgen en muchas industrias de producción donde se debe realizar la subdivisión de un material o espacio disponible en partes más pequeñas. Existe una gran variedad de métodos para resolver este tipo de problemas de optimización. A la hora de proponer un método de resolución para un problema de optimización, es recomendable tener en cuenta el enfoque y las necesidades que se tienen en relación al problema y su solución. Las aproximaciones exactas encuentran la solución óptima, pero sólo es viable aplicarlas a instancias del problema muy pequeñas. Las heurísticas manejan conocimiento específico del problema para obtener soluciones de alta calidad sin necesitar un excesivo esfuerzo computacional. Por otra parte, las metaheurísticas van un paso más allá, ya que son capaces de resolver una clase muy general de problemas computacionales. Finalmente, las hiperheurísticas tratan de automatizar, normalmente incorporando técnicas de aprendizaje, el proceso de selección, combinación, generación o adaptación de heurísticas más simples para resolver eficientemente problemas de optimización. Para obtener lo mejor de estos métodos se requiere conocer, además del tipo de optimización (mono o multi-objetivo) y el tamaño del problema, los medios computacionales de los que se dispone, puesto que el uso de máquinas e implementaciones paralelas puede reducir considerablemente los tiempos para obtener una solución. En las aplicaciones reales de los problemas de corte y empaquetado en la industria, la diferencia entre usar una solución obtenida rápidamente y usar propuestas más sofisticadas para encontrar la solución óptima puede determinar la supervivencia de la empresa. Sin embargo, el desarrollo de propuestas más sofisticadas y efectivas normalmente involucra un gran esfuerzo computacional, que en las aplicaciones reales puede provocar una reducción de la velocidad del proceso de producción. Por lo tanto, el diseño de propuestas efectivas y, al mismo tiempo, eficientes es fundamental. Por esta razón, el principal objetivo de este trabajo consiste en el diseño e implementación de métodos efectivos y eficientes para resolver distintos problemas de corte y empaquetado. Además, si estos métodos se definen como esquemas lo más generales posible, se podrán aplicar a diferentes problemas de corte y empaquetado sin realizar demasiados cambios para adaptarlos a cada uno. Así, teniendo en cuenta el amplio rango de metodologías de resolución de problemas de optimización y las técnicas disponibles para incrementar su eficiencia, se han diseñado e implementado diversos métodos para resolver varios problemas de corte y empaquetado, tratando de mejorar las propuestas existentes en la literatura. Los problemas que se han abordado han sido: el Two-Dimensional Cutting Stock Problem, el Two-Dimensional Strip Packing Problem, y el Container Loading Problem. Para cada uno de estos problemas se ha realizado una amplia y minuciosa revisión bibliográfica, y se ha obtenido la solución de las distintas variantes escogidas aplicando diferentes métodos de resolución: métodos exactos mono-objetivo y paralelizaciones de los mismos, y métodos aproximados multi-objetivo y paralelizaciones de los mismos. Los métodos exactos mono-objetivo aplicados se han basado en técnicas de búsqueda en árbol. Por otra parte, como métodos aproximados multi-objetivo se han seleccionado unas metaheurísticas multi-objetivo, los MOEAs. Además, para la representación de los individuos utilizados por estos métodos se han empleado codificaciones directas mediante una notación postfija, y codificaciones que usan heurísticas de colocación e hiperheurísticas. Algunas de estas metodologías se han mejorado utilizando esquemas paralelos haciendo uso de las herramientas de programación OpenMP y MPI. En el caso d

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Optimized shunting with mixed-usage tracks

We consider the planning of railway freight classification at hump yards, where the problem involves the formation of departing freight train blocks from arriving trains subject to scheduling and capacity constraints. The hump yard layout considered consists of arrival tracks of sufficient length at an arrival yard, a hump, classification tracks of non-uniform and possibly non-sufficient length at a classification yard, and departure tracks of sufficient length. To increase yard capacity, freight cars arriving early can be stored temporarily on specific mixed-usage tracks. The entire hump yard planning process is covered in this paper, and heuristics for arrival and departure track assignment, as well as hump scheduling, have been included to provide the neccessary input data. However, the central problem considered is the classification track allocation problem. This problem has previously been modeled using direct mixed integer programming models, but this approach did not yield lower bounds of sufficient quality to prove optimality. Later attempts focused on a column generation approach based on branch-and-price that could solve problem instances of industrial size. Building upon the column generation approach we introduce a direct arc-based integer programming model, where the arcs are precedence relations between blocks on the same classification track. Further, the most promising models are adapted for rolling-horizon planning. We evaluate the methods on historical data from the Hallsberg shunting yard in Sweden. The results show that the new arc-based model performs as well as the column generation approach. It returns an optimal schedule within the execution time limit for all instances but from one, and executes as fast as the column generation approach. Further, the short execution times of the column generation approach and the arc-indexed model make them suitable for rolling-horizon planning, while the direct mixed integer program proved to be too slow for this. Extended analysis of the results shows that mixing was only required if the maximum number of concurrent trains on the classification yard exceeds 29 (there are 32 available tracks), and that after this point the number of extra car roll-ins increases heavily

Meta-Heuristics for Dynamic Lot Sizing: a review and comparison of solution approaches

Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinational optimization problems. We review the various meta-heuristics that have been specifically developed to solve lot sizing problems, discussing their main components such as representation, evaluation neighborhood definition and genetic operators. Further, we briefly review other solution approaches, such as dynamic programming, cutting planes, Dantzig-Wolfe decomposition, Lagrange relaxation and dedicated heuristics. This allows us to compare these techniques. Understanding their respective advantages and disadvantages gives insight into how we can integrate elements from several solution approaches into more powerful hybrid algorithms. Finally, we discuss general guidelines for computational experiments and illustrate these with several examples

Nonlinear Integer Programming

Research efforts of the past fifty years have led to a development of linear integer programming as a mature discipline of mathematical optimization. Such a level of maturity has not been reached when one considers nonlinear systems subject to integrality requirements for the variables. This chapter is dedicated to this topic. The primary goal is a study of a simple version of general nonlinear integer problems, where all constraints are still linear. Our focus is on the computational complexity of the problem, which varies significantly with the type of nonlinear objective function in combination with the underlying combinatorial structure. Numerous boundary cases of complexity emerge, which sometimes surprisingly lead even to polynomial time algorithms. We also cover recent successful approaches for more general classes of problems. Though no positive theoretical efficiency results are available, nor are they likely to ever be available, these seem to be the currently most successful and interesting approaches for solving practical problems. It is our belief that the study of algorithms motivated by theoretical considerations and those motivated by our desire to solve practical instances should and do inform one another. So it is with this viewpoint that we present the subject, and it is in this direction that we hope to spark further research.Comment: 57 pages. To appear in: M. J\"unger, T. Liebling, D. Naddef, G. Nemhauser, W. Pulleyblank, G. Reinelt, G. Rinaldi, and L. Wolsey (eds.), 50 Years of Integer Programming 1958--2008: The Early Years and State-of-the-Art Surveys, Springer-Verlag, 2009, ISBN 354068274