Revisión del estado del arte del problema de ruteo abierto (OVRP)

En este documento se lleva a cabo una revisión bibliográfica del estado del arte del problema de ruteo abierto (OVRP; Open Vehicle Routing Problem). Se realiza la definición del problema, una clasificación de sus variantes y de los artículos e investigaciones publicadas en las bibliotecas virtuales: Scopus, Science Direct y Google Scholar acerca del tema. Además, se plantean los modelos de solución utilizados por los autores, las aplicaciones del estudio y las tendencias o futuras líneas de investigación. El OVRP es un problema de planificación de rutas de transporte, generalización del Problema del Agente Viajero muy conocido y ampliamente estudiado, tiene como característica diferenciadora que los vehículos una vez finalizadas las entregas correspondientes no están obligados a regresar al punto de partida o depósito. La revisión observa lo publicado hasta mayo del año 2017

Urban Navigation Handling Openstreetmap Data for an Easy to Drive Route

Atualmente, os cidadãos podem escolher as suas opções de viagem com base no tempo, distância, emissões, consumo, entre outros parâmetros. Não obstante, a literatura indica que os sistemas de planeamento de rotas atuais têm, maioritariamente, por base a distância e o tempo. Com efeito, verificou-se uma falta de sistemas de planeamento de rotas que se preocupem com as preferências dos utilizadores num ponto de vista mais qualitativo. Este projeto de investigação desenvolve um framework de planeamento de rotas com a integração de diferentes atributos da rede rodoviária como semáforos, passadeiras e paragens de autocarro, com o objetivo de providenciar aos utilizadores a opção de evitar estes mesmos atributos, oferecendo uma opção easy drive, nomeadamente em ambiente urbano. O estudo foi conduzido através de dados georreferenciados da cidade de Lisboa, Portugal. No entanto, é transferível para qualquer outra cidade. O algoritmo providencia alternativas para a rota mais curta, easy drive e rota balanceada, considerando apenas um modo de viagem: carro/mota. O modelo foi desenvolvido no PostgreSQL com a extensão PostGIS e PgRouting, e os resultados foram visualizados no software QGIS. O software permite customizar pesos para cada uma das restrições para a escolha das rotas e estes pesos são modificados com o objetivo de encontrar o caminho ótimo consoante as preferências de cada utilizador.Currently, citizens can choose their travel options based on time, distance, consumption, emission, among other parameters. Nevertheless, the literature indicates that current route planning systems are based on distance and time. In fact, there is a lack of route planning systems which are concerned with users' preferences from a more qualitative point of view. This research project develops a route planning framework with the integration of different road network features like traffic lights, pedestrian crossings, and bus stops, to provide users with the option to avoid these features, offering an easy drive option, namely in an urban environment. The study was conducted using georeferenced data from the city of Lisbon, Portugal. However, it is transferable to any other city. The algorithm provides alternatives for the shortest route, easy drive, and balanced route, considering only one travel mode: car/motorbike. The model was developed in PostgreSQL with the PostGIS extension and PgRouting, and the results were visualized in QGIS software. The software allows to custom weights for each of the constraints for route choices, and these weights are modified to find the optimal route according to the preferences of each user

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

Tese de doutoramento, Estatística e Investigação Operacional (Optimização), Universidade de Lisboa, Faculdade de Ciências, 2018Mass rescue operations (MRO) in maritime areas, particularly in ocean areas, are a major concern for the authorities responsible for conducting search and rescue (SAR) activities. A mass rescue operation can be defined as a search and rescue activity characterized by the need for immediate assistance to a large number of persons in distress, such that the capabilities normally available to search and rescue are inadequate. In this dissertation we deal with a mass rescue operation within ocean areas and we consider the problem of rescuing a set of survivors following a maritime incident (cruise ship, oil platform, ditched airplane) that are drifting in time. The recovery of survivors is performed by nearby ships and helicopters. We also consider the possibility of ships capable of refuelling helicopters while hovering which can extend the range to which survivors can be rescued. A linear binary integer formulation is presented along with an application that allows users to build instances of the problem. The formulation considers a discretization of time within a certain time step in order to assess the possibility of travelling along different locations. The problem considered in this work can be perceived as an extension of the generalized vehicle routing problem (GVRP) with a profit stance since we may not be able to recover all of the survivors. We also present a look ahead approach, based on the pilot method, to the problem along with some optimal results using state of the art Mixed-integer linear programming solvers. Finally, the efficacy of the solution from the GVRP is estimated for a set of scenarios that combine incident severity, location, traffic density for nearby ships and SAR assets availability and location. Using traffic density maps and the estimated MRO efficacy, one can produce a combined vulnerability map to ascertain the quality of response to each scenario.Marinha Portuguesa, Plano de Atividades de Formação Nacional (PAFN