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http://repositorio.ufc.br/handle/riufc/71925| Tipo: | Artigo de Periódico |
| Título: | Variable fixing heuristics for solving multiple depot vehicle scheduling problem with heterogeneous fleet and time windows |
| Autor(es): | Dauer, Armando Teles Prata, Bruno de Athayde |
| Palavras-chave: | Combinatorial optimization;Public transport systems;Mixed integer linear programming;Machine learning |
| Data do documento: | 2021 |
| Instituição/Editor/Publicador: | Optimization Letters |
| Citação: | DAUER, Armando Teles; PRATA, Bruno de Athayde. Variable fixing heuristics for solving multiple depot vehicle scheduling problem with heterogeneous fleet and time windows. Optimization Letters, [S. l.], v. 15, p. 153-170, 2021. |
| Abstract: | This paper aims at presenting the multiple depot vehicle scheduling problem with heterogeneous fleet and time windows (MDHFVSP-TW). We used a time-space network (TSN) to perform the modeling of MDHFVSP-TW, along with two methodologies to reduce its size and, therefore, its complexity. Along with size reduction methods, a mixed integer programming (MIP) heuristic with variable fixation was presented. Its operation is based on the use of the solution for this problem with relaxed variables as a basis for the removal of arcs from the problem, reducing its size and enabling its resolution in reasonable computational time. Extensive tests were performed for a collection of randomly generated instances. Subsequently, a case study arising from a real instance from a Brazilian city is presented. The computational results showed that the proposed heuristic and size reduction methods obtained good performance, providing high-quality solutions in an adequate computational time. |
| URI: | http://www.repositorio.ufc.br/handle/riufc/71925 |
| ISSN: | 1862-4480 |
| Tipo de Acesso: | Acesso Aberto |
| Aparece nas coleções: | DEHA - Artigos publicados em revista científica |
Arquivos associados a este item:
| Arquivo | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| 2021_art_atdauer1.pdf | 301,75 kB | Adobe PDF | Visualizar/Abrir |
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