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

Jiabin Luo, Yue Wu, Andre Mendes

Research output: Contribution to journalArticle

Abstract

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.
Original languageEnglish
Pages (from-to)32-44
JournalComputers and Industrial Engineering
Volume94
Early online date23 Jan 2016
DOIs
Publication statusPublished - 1 Apr 2016

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Unloading
Containers
Scheduling
Genetic algorithms
Integer programming
Ships
Automation
Experiments

Cite this

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title = "Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal",
abstract = "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.",
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AB - 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.

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