Robust covering problems: formulations, algorithms and application
Two robust optimization NP-Hard problems are studied in this thesis: the min-max regret WSCP and the min-max regret MCLP. The uncertain data in these problems is modeled by intervals and only the minimum and maximum values for each interval are known. While the min-max regret WSCP is still a theoret...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | Brasil |
| Institución: | Universidade Federal de Minas Gerais (UFMG) |
| Repositorio: | Repositório Institucional da UFMG |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufmg.br:1843/ESBF-AYUMW2 |
| Acceso en línea: | http://hdl.handle.net/1843/ESBF-AYUMW2 |
| Access Level: | acceso abierto |
| Palabra clave: | Algoritmos Logística Meta-heurísticas Pesquisa Operacional Otimização Combinatória Algorítmos Otimização combinatória Pesquisa operacional Computação |
| Sumario: | Two robust optimization NP-Hard problems are studied in this thesis: the min-max regret WSCP and the min-max regret MCLP. The uncertain data in these problems is modeled by intervals and only the minimum and maximum values for each interval are known. While the min-max regret WSCP is still a theoretical problem, the min-max regret MCLP has an application in disaster logistics which is investigated in this thesis. Four mathematical formulations, three exact algorithms and five heuristics were developed and applied to both problems. Computational experiments showed that the exact algorithms efficiently solved 14 out of 75 instances generated to the min-max regret WSCP and all realistic instances created to the min-max regret MCLP. For the simulated instances that was not solved to optimally in both problems, the heuristics developed in this thesis found solutions, as good as, or better than the best exact algorithm in almost all instance. |
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