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

Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals
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.
container terminal operations, automated container terminal, scheduling, container storage, dual-cycle
1366-5545
49-64
Luo, Jiabin
28f8ad64-665b-41b3-8347-bba8b839a3f8
Wu, Yue
e279101b-b392-45c4-b894-187e2ded6a5c
Luo, Jiabin
28f8ad64-665b-41b3-8347-bba8b839a3f8
Wu, Yue
e279101b-b392-45c4-b894-187e2ded6a5c

Luo, Jiabin and Wu, Yue (2015) Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals. Transportation Research Part E: Logistics and Transportation Review, 79, 49-64. (doi:10.1016/j.tre.2015.03.006).

Record type: Article

Abstract

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.

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Accepted/In Press date: 19 March 2015
e-pub ahead of print date: 21 April 2015
Published date: July 2015
Keywords: container terminal operations, automated container terminal, scheduling, container storage, dual-cycle
Organisations: Faculty of Business, Law and Art

Identifiers

Local EPrints ID: 378993
URI: http://eprints.soton.ac.uk/id/eprint/378993
ISSN: 1366-5545
PURE UUID: 46d50203-fc3d-4667-a703-817cd49c1c10
ORCID for Yue Wu: ORCID iD orcid.org/0000-0002-1881-6003

Catalogue record

Date deposited: 20 Jul 2015 15:31
Last modified: 15 Mar 2024 03:20

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Contributors

Author: Jiabin Luo
Author: Yue Wu ORCID iD

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