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

• Published in: Computers & industrial engineering Vol. 94; pp. 32 - 44
• Main Authors: Luo, Jiabin, Wu, Yue, Mendes, André Bergsten
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.04.2016; Pergamon Press Inc
• Subjects: Automated container terminal; Automation; Computer programs; Container storage; Container unloading; Containerization; Containers; Genetic algorithms; Integer programming; Mathematical models; Scheduling; Studies; Transport buildings, stations and terminals; Transportation terminals; Vehicle scheduling; Vehicles; Integer programming; Vehicle scheduling; Automated container terminal; Container storage; Container unloading
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2016.01.010

•We model the integrated vehicle scheduling and container storage problem.•Container unloading process in an automated container terminal is considered.•Small-sized problems are solved optimally by existing software.•Genetic algorithm is developed to solve Large-sized problems.•The results show the efficiency of the proposed modelling approach and algorithm. 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.