Variable fixing mip heuristics for solving multiple depot vehicle scheduling problem with heterogeneous fleet and time windows

Detalhes bibliográficos
Ano de defesa: 2019
Autor(a) principal: Dauer, Armando Teles
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/43332
Resumo: The multiple depot heterogeneous fleet vehicle scheduling problem (MDHFVSP) consists of allocating vehicles for predetermined trips groups, taking into account multiple depots, the capacity of these depots, different types of vehicles as well as trips of various demands and vehicles with different capacities. The main objective of a transportation system planning is to reduce costs, implementation and/or operation costs, reducing vehicle utilization and minimizing fuel and crew costs. This thesis proposes a new variant of the MDHFVSP that considers the application of 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.