Caminho mínimo com restrição probabilística de atraso máximo
In the Probabilistic Delay Constrained Shortest Path problem we aim to consider the time factor in the design of cargo routing paths in road networks at minimum cost, considering the increasing uncertainty in travel times of these routes in real networks, and keeping in mind strategies of quality of...
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| Formato: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Brasil |
| Recursos: | Universidade Federal do Ceará (UFC) |
| Repositorio: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufc.br:riufc/17331 |
| Acesso em linha: | http://www.repositorio.ufc.br/handle/riufc/17331 |
| Access Level: | acceso abierto |
| Palavra-chave: | Matematica discreta e combinatória Caminho mínimo Restrição probabilística Otimização inteira Shortest path Probabilistic constraint Integer optimization |
| Resumo: | In the Probabilistic Delay Constrained Shortest Path problem we aim to consider the time factor in the design of cargo routing paths in road networks at minimum cost, considering the increasing uncertainty in travel times of these routes in real networks, and keeping in mind strategies of quality of service, in order to obtain a compromise between the travel costs and the compliance of the arrival time at the destination. We conducted a study of related problems in the literature of transport networks optimization, in order to better understand the problem to be addressed, about which we are not aware of existing works. We developed a scheme for enumerating partitions of the solution space of this problem, which uses an L decomposition to select these partitions wisely, and is aided by solutions to relaxations of the problem to obtain bounds for the optimal cost. In addition, we developed some branching and pruning strategies for a Branch-and-Bound scheme, with a pre-processing phase, in order to try and solve the problem directly. The computational results show that we are competitive with the commercial tool used for comparison in the smaller instances. For the remaining instances, this tool is more efficient in the time required for solving the problem. |
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