Algoritmo genérico com chaves aleatórias viciadas para problemas de otimização em portos

• Ano de defesa: 2014
• Autor(a) principal: Pomari, Carlos Zaca [UNIFESP]
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Paulo (UNIFESP)
• Palavras-chave em Português: combinatorial optimization; biased random key genetic algorithm; berth allocation problem; clustering search; metaheuristic; maritime transport; otimização combinatória; algoritmo genético com chaves aleatórias viciadas; problema de alocação de berços; metaheurística; transporte marítimo
• Link de acesso: https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=1325140; https://repositorio.unifesp.br/handle/11600/46510

The aim of this work is to address one of the problems that is present in the operation of many ports and that influences their efficiency: The berth allocation problem (BAP), which seeks to minimize the vessels? handling times, indicating their order of berthing and their location on the quay. This problem has strong theoretical and practical interests, since the growth of the world economy and international trade in goods has stimulated, in recent years, the demand for shipping services. The methodology for the development of this work consists in studying and applying a hybrid algorithm based on Biased Random Key Genetic Algorithm (BRKGA) and Clustering Search (CS) metaheuristics to heuristically solve the BAP. A BRKGA searches the solution space of the combinatorial optimization problem indirectly, therefore, it is necessary to specify how solutions are encoded and decoded and how their corresponding fitness values are computed. This study uses the BRKGA as solutions generator to the CS? clustering process. To validate the proposed method, computational tests are performed with instances available in the literature and a case study of the BAP with operating data from Tubarão-ES port.