Paralelismo aplicado a Ant Colony Optimization

La utilización de metaheurísticas para la resolución de problemas de optimización combinatoria del tipo NP-difícil ha permitido afrontar instancias grandes obteniendo soluciones cercanas al óptimo en tiempos razonables. En los últimos años la aplicación de paralelismo a las metaheurísticas ha demostrado su utilidad no solamente porque ha permitido disminuir considerablemente los tiempos de ejecución, sino también porque ha permitido obtener mejoras en la calidad de las soluciones encontradas. Ant Colony Optimization (ACO) es una metaheurística de las más recientes que ha sido aplicada con éxito sobre varios de los problemas estándares de optimización demostrando su potencial. Las primeras propuestas de paralelismo aplicado a ACO se remontan a los orígenes de la propia metaheurística. Sin embargo, la investigación en esta temática ha crecido notablemente en los últimos cinco años. El presente reporte es un relevamiento sobre la aplicación de técnicas de alto desempeño sobre ACO. El objetivo de este relevamiento es brindar un resumen de las principales propuestas existentes en la literatura sobre esta temática. Solamente se consideran las implementaciones paralelas aplicadas sobre problemas estáticos monobjetivos de optimización combinatoria

Solución de problemas estocásticos de localización-ruteo

96 Páginas.El problema de localización-ruteo estocástico (SLRP por sus siglas en inglés) es un problema muy común en empresas de manufactura, comercializadoras y transportadoras. El problema consiste en simultáneamente localizar uno o varios depósitos centrales entre un conjunto de ubicaciones potenciales, determinar un tamaño de flota y diseñar rutas para cada unos de los vehículos para visitar un conjunto de clientes considerando la incertidumbre que existe en algunos aspectos de la operación. En las soluciones presentadas en la literatura para este tipo de problemas se ha considerado mayoritariamente soluciones determinísticas o las soluciones estocásticas presentadas solo consideran en su mayoría la demanda como componente estocástico del sistema. La presente investigación propone un modelo para resolver la versión estocástica con incertidumbre en los costos de transporte y velocidades de los vehículos a través de un enfoque jerárquico de dos fases basado tanto en optimización como en simulación de eventos discretos. Se presenta una estrategia de selección aleatoria en la fase de localización; la fase de ruteo se resuelve empleando un algoritmo basado en colonia de hormigas, y finalmente se incluye al modelo el comportamiento estocástico del sistema a través de simulación de eventos discretos. Se presenta un análisis comparativo para validar la calidad de las soluciones obtenidas por el algoritmo y se realiza un estudio experimental permitiendo el análisis estadístico de resultados. Los resultados obtenidos permiten validar el presente enfoque como una buena herramienta de apoyo a la toma de decisiones para la localización de centros de distribución, la determinación de flotas de vehículos, la asignación de zonas de servicio y el ruteo de vehículos

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented

GAPS : a hybridised framework applied to vehicle routing problems

In this thesis we consider two combinatorial optimisation problems; the Capacitated Vehicle Routing Problem (CVRP) and the Capacitated Arc Routing Problem (CARP). In the CVRP, the objective is to find a set of routes for a homogenous fleet of vehicles, which must service a set of customers from a central depot. In contrast, the CARP requires a set of routes for a fleet of vehicles to service a set of customers at the street level of an intercity network. After a comprehensive discussion of the existing exact and heuristic algorithmic techniques presented in the literature for these problems, computational experiments to provide a benchmark comparison of a subset of algorithmic implementations for these methods are presented for both the CVRP and CARP, run against a series of dataset instances from the literature. All dataset instances are re-catalogued using a standard format to overcome the difficulties of the different naming schemes and duplication of instances that exist between different sources. We then present a framework, which we shall call Genetic Algorithm with Perturbation Scheme (GAPS), to solve a number of combinatorial optimisation problems. The idea is to use a genetic algorithm as a container framework in conjunction with a perturbation or weight coding scheme. These schemes make alterations to the underlying input data within a problem instance, after which the changed data is fed into a standard problem specific heuristic and the solution obtained decoded to give a true solution cost using the original unaltered instance data. We first present GAPS in a generic context, using the Travelling Salesman Problem (TSP) as an example and then provide details of the specific application of GAPS to both the CVRP and CARP. Computational experiments on a large set of problem instances from the literature are presented and comparisons with the results achieved by the current state of the art algorithmic approaches for both problems are given, highlighting the robustness and effectiveness of the GAPS framework.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

GAPS: a hybridised framework applied to vehicle routing problems

In this thesis we consider two combinatorial optimisation problems; the Capacitated Vehicle Routing Problem (CVRP) and the Capacitated Arc Routing Problem (CARP). In the CVRP, the objective is to find a set of routes for a homogenous fleet of vehicles, which must service a set of customers from a central depot. In contrast, the CARP requires a set of routes for a fleet of vehicles to service a set of customers at the street level of an intercity network. After a comprehensive discussion of the existing exact and heuristic algorithmic techniques presented in the literature for these problems, computational experiments to provide a benchmark comparison of a subset of algorithmic implementations for these methods are presented for both the CVRP and CARP, run against a series of dataset instances from the literature. All dataset instances are re-catalogued using a standard format to overcome the difficulties of the different naming schemes and duplication of instances that exist between different sources. We then present a framework, which we shall call Genetic Algorithm with Perturbation Scheme (GAPS), to solve a number of combinatorial optimisation problems. The idea is to use a genetic algorithm as a container framework in conjunction with a perturbation or weight coding scheme. These schemes make alterations to the underlying input data within a problem instance, after which the changed data is fed into a standard problem specific heuristic and the solution obtained decoded to give a true solution cost using the original unaltered instance data. We first present GAPS in a generic context, using the Travelling Salesman Problem (TSP) as an example and then provide details of the specific application of GAPS to both the CVRP and CARP. Computational experiments on a large set of problem instances from the literature are presented and comparisons with the results achieved by the current state of the art algorithmic approaches for both problems are given, highlighting the robustness and effectiveness of the GAPS framewor