Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal

Effectively scheduling vehicles and allocating storage locations for containers are two important problems in container terminal operations. Early research efforts, however, are devoted to study them separately. This paper investigates the integration of the two problems focusing on the unloading process in an automated container terminal, where all or part of the equipment are built in automation. We formulate the integrated problem as a mixed-integer programming (MIP) model to minimise ship’s berth time. We determine the detailed schedules for all vehicles to be used during the unloading process and the storage location to be assigned for all containers. A series of experiments are carried out for small-sized problems by using commercial software. A genetic algorithm (GA) is designed for solving large-sized problems. The solutions from the GA for the small-sized problems are compared with the optimal solutions obtained from the commercial software to verify the effectiveness of the GA. The computational results show that the model and solution methods proposed in this paper are efficient in solving the integrated unloading problem for the automated container terminal

Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals

This study proposes a new approach to determine the dispatching rules of AGVs and container storage locations, considering both unloading and loading processes simultaneously. We formulate this problem as a mixed integer programming model, aiming to minimise the ship’s berth time. Optimal solutions can be obtained in small sizes, however, large-sized problems are hard to solve optimally in a reasonable time. Therefore, a heuristic method, i.e. genetic algorithm is designed to solve the problem in large sizes. A series of numerical experiments are carried out to evaluate the effectiveness of the integration approach and algorithm

Historia, evolución y perspectivas de futuro en la utilización de técnicas de simulación en la gestión portuaria: aplicaciones en el análisis de operaciones, estrategia y planificación portuaria

Programa Oficial de Doutoramento en Análise Económica e Estratexia Empresarial. 5033V0[Resumen] Las técnicas de simulación, tal y como hoy las conocemos, comenzaron a mediados del siglo XX; primero con la aparición del primer computador y el desarrollo del método Monte Carlo, y más tarde con el desarrollo del primer simulador de propósito específico conocido como GPS y desarrollado por Geoffrey Gordon en IBM y la publicación del primer texto completo dedicado a esta materia y llamado the Art of Simulation (K.D. Tocher, 1963). Estás técnicas han evolucionado de una manera extraordinaria y hoy en día están plenamente implementadas en diversos campos de actividad. Las instalaciones portuarias no han escapado de esta tendencia, especialmente las dedicadas al tráfico de contenedores. Efectivamente, las características intrínsecas de este sector económico, le hacen un candidato idóneo para la implementación de modelos de simulación con propósitos y alcances muy diversos. No existe, sin embargo y hasta lo que conocemos, un trabajo científico que compile y analice pormenorizadamente tanto la historia como la evolución de simulación en ambientes portuarios, ayudando a clasificar los mismos y determinar cómo estos pueden ayudar en el análisis económico de estas instalaciones y en la formulación de las oportunas estrategias empresariales. Este es el objetivo último de la presente tesis doctoral.[Resumo] As técnicas de simulación, tal e como hoxe as coñecemos, comezaron a mediados do século XX; primeiro coa aparición do computador e o desenvolvemento do método Monte Carlo e máis tarde co desenvolvemento do primeiro simulador de propósito específico coñecido como GPS e desenvolvido por Geoffrey Gordon en IBM e a publicación do primeiro texto completo dedicado a este tema chamado “A Arte da Simulación” (K.D. Tocher, 1963). Estas técnicas evolucionaron dun xeito extraordinario e hoxe en día están plenamente implementadas en diversos campos de actividade. As instalacións portuarias non escaparon desta tendencia, especialmente as dedicadas ao tráfico de contenedores. Efectivamente, as características intrínsecas deste sector económico, fanlle un candidato idóneo para a implementación de modelos de simulación con propósitos e alcances moi variados. Con todo, e ata o que coñecemos, non existe un traballo científico que compila e analiza de forma detallada tanto a historia como a evolución da simulación en estes ambientes portuarios, clasificando os mesmos e determinando como estes poden axudar na análise económica destas instalacións e na formulación das oportunas estratexias empresariais. Este é o último obxectivo da presente tese doutoral.[Abstract] Simulation, to the extend that we understand it nowadays, began in the middle of the 20th century; first with the appearance of the computer and the development of the Monte Carlo method, and later with the development of the first specific purpose simulator known as GPS developed by Geoffrey Gordon in IBM. This author published the first full text devoted to this subject “The Art of Simulation” in 1963. These techniques have evolved in an extraordinary way and nowadays they are fully implemented in different fields of activity. Port facilities have not escaped this trend, especially those dedicated to container traffic. Indeed, the intrinsic characteristics of this economic sector, make it a suitable candidate for the implementation of simulation with very different purposes and scope. However, to the best of our knowelegde, there is not a scientific work that compiles and analyzes in detail both, the history and the evolution of simulation in port environments, contributing to classify them and determine how they can help in the economic analysis of these facilities and in the formulation of different business strategies. This is the ultimate goal of this doctoral thesis

The General Yard Allocation Problem

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)27241986-199