8 research outputs found

    Towards the landscape rotation as a perturbation strategy on the quadratic assignment problem.

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    Recent work in combinatorial optimisation have demonstrated that neighbouring solutions of a local optima may belong to more favourable attraction basins. In this sense, the perturbation strategy plays a critical role on local search based algorithms to kick the search of the algorithm into more prominent areas of the space. In this paper, we investigate the landscape rotation as a perturbation strategy to redirect the search of an stuck algorithm. This technique rearranges the mapping of solutions to different objective values without altering important properties of the problem's landscape such as the number and quality of optima, among others. Particularly, we investigate two rotation based perturbation strategies: (i) a profoundness rotation method and (ii) a broadness rotation method. These methods are applied into the stochastic hill-climbing heuristic and tested and compared on different instances of the quadratic assignment problem against other algorithm versions. Performed experiments reveal that the landscape rotation is an efficient perturbation strategy to shift the search in a controlled way. Nevertheless, an empirical investigation of the landscape rotation demonstrates that it needs to be cautiously manipulated in the permutation space since a small rotation does not necessarily mean a small disturbance in the fitness landscape

    Combinatorial Ant Optimization and the Flowshop Problem

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    Researchers have developed efficient techniques, meta-heuristics to solve many Combinatorial Optimization (CO) problems, e.g., Flow shop Scheduling Problem, Travelling Salesman Problem (TSP) since the early 60s of the last century. Ant Colony Optimization (ACO) and its variants were introduced by Dorigo et al. [DBS06] in the early 1990s which is a technique to solve CO problems. In this thesis, we used the ACO technique to find solutions to the classic Flow shop Scheduling Problem and proposed a novel method for solution improvement. Our solution is composed of two phases; in the first phase, we solved TSP using ACO technique which gave us an initial permutation or tour. We used the same trip as an initial solution for our problem and then improved it by using 2-opt exchanges which yielded in a promising result. Furthermore, we introduced another improvement technique which gave us a more promising result. We have compared our results with the best (optimal) and worst solution known till date. A comprehensive experimental study using existing dataset proves that our approach remarkably gives good results

    Metodología de algoritmos meméticos para el problema de ruteo de vehículos con entregas parciales y tiempos de viaje dependientes con ventanas de tiempo

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    El problema de ruteo de vehículos VRP es uno de los problemas más estudiados en investigación de operaciones, dada su relevancia en los campos del transporte y la logística. En los últimos años ha aumentado el interés en minimizar la contaminación por la emisión de gases efecto invernadero a causa del consumo de combustibles fósiles. El sector transporte representa una parte importante en esas emisiones. En el transporte, situaciones como los embotellamientos en las horas pico, por ejemplo, conducen a una red vial dinámica en la que varían los tiempos de viaje y consecuentemente el consumo de combustible. Por lo anterior el problema de enrutamiento de vehículos con tiempos dependientes TDVRP es una representación más cercana la vida real que los modelos tradicionales de enrutamientos de vehículos, VRP. Por otro lado, el problema de enrutamiento de vehículos con partición de entregas, SDVRP permite asignar múltiples rutas a un mismo cliente, propiciando ahorros en las mismas. El objetivo de esta tesis es desarrollar un método para el uso de los recursos de transporte, con el fin de atender a los clientes de manera eficiente respecto al costo total de la distancia recorrida y al tiempo total de viaje requerido. El problema consiste en programar un recorrido durante un día dividido en intervalos o zonas horarias, con ventanas de tiempo para atender a cada cliente, vehículos homogéneos con capacidad fija Q y un depósito único. Para ello se propone en este trabajo un Algoritmo Memético (MA) capaz de encontrar soluciones que respetan las restricciones del problema, teniendo en cuenta la posibilidad de hacer particiones en las entregas. Mediante el Diseño de Experimentos se evaluó la calidad de las soluciones generadas respecto a un Algoritmo Genético (GA) desarrollado también para el propósito, teniendo como criterio de evaluación el porcentaje de mejores soluciones alcanzado por cada algoritmo. Los experimentos permiten afirmar que el Algoritmo Memético propuesto supera el Algoritmo Genético, resultando más robusto ante cambios en los parámetros de ambos métodos. La solución propuesta representa un modelo más cercano a la realidad de las redes viales y genera rutas tendientes a disminuir la cantidad, recorrido y tiempo de permanencia de los vehículos en la red vial, conllevando a la disminución de las emisiones de gases efecto invernadero.Abstract: The vehicle routing problem VRP is one of the most studied problems in operations research, given its relevance in the fields of transport and logistics. In recent years, interest in minimizing pollution due to the emission of greenhouse gases, as a result of the consumption of fossil fuels, has increased. The transport sector represents an important part of those emissions. In transport, situations such as traffic jams during peak hours, for example, lead to a dynamic road network in which travel times and consequently fuel consumption vary. Therefore, the time dependent vehicle routing problem, TDVRP, is a closer representation of real life than the traditional vehicle routing models, VRP. On the other hand, the split delivery vehicle routing problem, SDVRP, allows assigning multiple routes to the same client, promoting savings in them. The objective of this thesis is to develop a method for the use of transportation resources, in order to serve customers efficiently with regard to the total cost of the distance traveled and the total traveled time required. The problem consists of scheduling a trip for a day which is divided into intervals or time zones, with time windows to serve each customer, homogeneous vehicles with fixed capacity Q and a single deposit. In order to do so, a Memetic Algorithm (MA) is proposed in this work, capable of finding solutions that respect the constraints of the problem, taking into account the possibility of splitting the deliveries. By using Design of Experiments, the quality of the solutions generated by the Memetic Algorithm was evaluated with respect to a Genetic Algorithm (GA) also developed for the purpose, having as the evaluation criterion the percentage of best solutions reached by each algorithm. The experiments show that the proposed memetic algorithm surpasses the genetic algorithm, being more robust to changes in the parameters of both methods The proposed solution represents a model that is closer to the reality of road networks and generates routes that tend to reduce quantity, travel length and time spent by vehicles on the road network, leading to a reduction in greenhouse gas emissions.Maestrí

    Improving taxi dispatch services with real-time traffic and customer information

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    Ph.DDOCTOR OF PHILOSOPH

    Paths and compatible hamiltonian cycles

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    Aquest projecte està centrat en estudiar condicions que fan que un conjunt de punts tinguin un camí d'expansió que sigui compatible amb un cicle Hamiltonià. Hem demostrat que ser un camí monòton o self-approaching és condició suficient per asegurar que hi ha un cicle Hamiltonià compatible. A més, hem estudiat la condició de ser un camí que coincideix amb el MST del conjunt de punts i demostrat alguns resultats interessants per ajudar en futurs investigacions per demostrar que aquesta condició és suficient

    Free-flyer path planning in the proximity to large space structures

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    The development of the modem space stations into large, highly complex orbital structures such as the International Space Station (ISS), has brought about a requirement for free-flying vehicles to perform various inspection and maintenance task on the exterior of the station. Concentrating on the ISS-Inspector vehicle, this thesis investigates the trajectory and mission planning required for a small free-flying vehicle operating in close proximity to the ISS. Two complimentary methods are presented to permit safe manoeuvring around the ISS. Ellipse of Safety trajectories enforce long-term passive safety requirements in the presence of differential air drag during the fly-around phases of the mission, used to transfer between the docking port and observation points. Short-range, close proximity manoeuvring is permitted through the use of Potential Field Guidance methods, enhanced through Velocity Selection strategies to provide passively safe trajectories where possible. Finally, a mission planning tool is presented to permit the integrated planning of ISS-Inspector missions, with automated scheduling and trajectory selection, designed to optimise the use of available manoeuvring methods to maximise overall mission safety. This facilitates the rapid planning and prototyping of Inspector missions from within a single tool, which is available both to operators on the ground and the crew onboard the ISS
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