research-article

Real-time scheduling of twin stacking cranes in an automated container terminal using a genetic algorithm

Authors:

Ri Choe,

Hui Yuan,

Youngjee Yang,

Kwang Ryel RyuAuthors Info & Claims

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

Pages 238 - 243

https://doi.org/10.1145/2245276.2245323

Published: 26 March 2012 Publication History

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Abstract

We address the problem of scheduling twin automated stacking cranes (ASCs) used in automated container terminals. By extending the previous works, we show that it is important to make explicit the hidden jobs needed to prepare for the main requested jobs. Since the preparatory jobs can be done by any of the two ASCs, appropriate assignment of these jobs can help to promote cooperation and avoid interference between the two ASCs. The proposed genetic algorithm (GA) performs search within the framework of iterative rescheduling to cope with the uncertainty of ASC operation. To boost the search performance under tight real-time constraint of iterative rescheduling, our GA uses some of the solutions of the previous iteration to initialize the population of the current iteration. It has also been shown that our GA performs more robustly than other algorithm such as simulated annealing in an uncertain environment.

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Cited By

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Zhao NLodewijks GFu ZSun YSun Y(2022)Trajectory Predictions with Details in a Robotic Twin-Crane SystemComplex System Modeling and Simulation10.23919/CSMS.2021.00282:1(1-17)Online publication date: Mar-2022
https://doi.org/10.23919/CSMS.2021.0028
Karder JBeham AWerth BWagner SAffenzeller M(2022)Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service TimesProcedia Computer Science10.1016/j.procs.2022.01.302200(1031-1040)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.01.302
Nath RNagaraju A(2021)Genetic algorithm based on-arrival task scheduling on distributed computing platformInternational Journal of Computers and Applications10.1080/1206212X.2021.197475144:9(887-896)Online publication date: 12-Sep-2021
https://doi.org/10.1080/1206212X.2021.1974751
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Real-time scheduling of twin stacking cranes in an automated container terminal using a genetic algorithm

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Information

Published In

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

March 2012

2179 pages

ISBN:9781450308571

DOI:10.1145/2245276

Conference Chairs:
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Paola Lecca
The Microsoft Research - University of Trento COSBI, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2012

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SAC 2012

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SIGAPP

SAC 2012: ACM Symposium on Applied Computing

March 26 - 30, 2012

Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Sponsor:
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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

View all

Zhao NLodewijks GFu ZSun YSun Y(2022)Trajectory Predictions with Details in a Robotic Twin-Crane SystemComplex System Modeling and Simulation10.23919/CSMS.2021.00282:1(1-17)Online publication date: Mar-2022
https://doi.org/10.23919/CSMS.2021.0028
Karder JBeham AWerth BWagner SAffenzeller M(2022)Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service TimesProcedia Computer Science10.1016/j.procs.2022.01.302200(1031-1040)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.01.302
Nath RNagaraju A(2021)Genetic algorithm based on-arrival task scheduling on distributed computing platformInternational Journal of Computers and Applications10.1080/1206212X.2021.197475144:9(887-896)Online publication date: 12-Sep-2021
https://doi.org/10.1080/1206212X.2021.1974751
Briskorn D(2021)Routing two stacking cranes with predetermined container sequencesJournal of Scheduling10.1007/s10951-021-00689-4Online publication date: 19-Jun-2021
https://doi.org/10.1007/s10951-021-00689-4
Huang SLi Y(2016)A Cooperative Approach to Dispatching and Scheduling Twin-Yard Cranes in Container TerminalsComputational Logistics10.1007/978-3-319-44896-1_10(146-158)Online publication date: 14-Aug-2016
https://doi.org/10.1007/978-3-319-44896-1_10
Choe RKim TKim TRyu K(2013)Crane scheduling for opportunistic remarshaling of containers in an automated stacking yardFlexible Services and Manufacturing Journal10.1007/s10696-013-9186-327:2-3(331-349)Online publication date: 5-Dec-2013
https://doi.org/10.1007/s10696-013-9186-3

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