Alocação de recursos em nível operacional com incerteza nos dados

• Ano de defesa: 2012
• Autor(a) principal: Lima, Matheus Garibalde Soares de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Programa de Pós-Graduação em Engenharia Mecânica e de Materiais
• Palavras-chave em Português: Pesquisa operacional; Programação heurística; Otimização combinatória; Incerteza (Economia); Alocação de recursos; Transporte de mercadorias - Processo decisório; Simulação (Computadores); Engenharia mecânica; Operations research; Heuristic programming; Combinatorial optimization; Uncertainty; Resource allocation; Shipment of goods - Decision making; Computer simulation; Mechanical engineering
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/1298

The study aims to address the allocation of resources at the operational level under uncertainties. For this reason, it was proposed an optimization approach based on heuristic methods. The resolutions of production and logistics problems, commonly addressed in operational research, explore various parameters among which the present study considers three variables of uncertainty: demand, operation time and resources availability. For this purpose a logistics problem was chosen as study of case. The problem consists in minimizing cost operation, selection of vehicles in a heterogeneous fleet, consolidation of loads for each client and selecting the type of freight payables. Regarding of freight payables types, there are centered in two different tariffs, mainly due to assets and service negotiation, such as: i) fleet controlled by company and service outsource; ii) fleet and service completely outsource. The resolution of the original problem was broke down in two steps: i) Compartmentalizer and ii) Allocator. Both steps are solved through Tabu Search approach; the first step (Compartmentalizer) generates a list of feasible shipments to fulfill orders up to three different customers. The second step, the allocator uses the list of feasible shipments to define how and when each request will be supplied. The results aim the feasibility of assumes this approach in order to solve real problems.