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A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals

Commonly in container terminals, the containers are stored in yards on top of each other using yard cranes. The split-platform storage/retrieval system (SP-AS/RS) has been invented to store containers more efficiently and to access them more quickly. The integrated scheduling of quay cranes, automat...

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Prif Awduron: Homayouni, Seyed Mahdi, Tang, Sai Hong, Motlagh, Omid Reza Esmaeili
Fformat: Erthygl
Iaith:English
Cyhoeddwyd: Elsevier 2014
Mynediad Ar-lein:http://psasir.upm.edu.my/id/eprint/36164/1/A%20genetic%20algorithm%20for%20optimization%20of%20integrated%20scheduling%20of%20cranes%2C%20vehicles%2C%20and%20storage%20platforms%20at%20automated%20container%20terminals.pdf
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id oai:psasir.upm.edu.my:36164
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spelling oai:psasir.upm.edu.my:36164 http://psasir.upm.edu.my/id/eprint/36164/ A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals Homayouni, Seyed Mahdi Tang, Sai Hong Motlagh, Omid Reza Esmaeili Commonly in container terminals, the containers are stored in yards on top of each other using yard cranes. The split-platform storage/retrieval system (SP-AS/RS) has been invented to store containers more efficiently and to access them more quickly. The integrated scheduling of quay cranes, automated guided vehicles and handling platforms in SP-AS/RS has been formulated and solved using the simulated annealing algorithm in previous literatures. This paper presents a genetic algorithm (GA) to solve this problem more accurately and precisely. The GA includes a new operator to make a random string of tasks observing the precedence relations between the tasks. For evaluating the performance of the GA, 10 small size test cases were solved by using the proposed GA and the results were compared to those from the literature. Results show that the proposed GA is able to find fairly near optimal solutions similar to the existing simulated annealing algorithm. Moreover, it is shown that the proposed GA outperforms the existing algorithm when the number of tasks in the scheduling horizon increases (e.g. 30 to 100). Elsevier 2014-11 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/36164/1/A%20genetic%20algorithm%20for%20optimization%20of%20integrated%20scheduling%20of%20cranes%2C%20vehicles%2C%20and%20storage%20platforms%20at%20automated%20container%20terminals.pdf Homayouni, Seyed Mahdi and Tang, Sai Hong and Motlagh, Omid Reza Esmaeili (2014) A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals. Journal of Computational and Applied Mathematics, 270. pp. 545-556. ISSN 0377-0427; ESSN: 1879-1778 10.1016/j.cam.2013.11.021
institution UPM IR
collection UPM IR
language English
description Commonly in container terminals, the containers are stored in yards on top of each other using yard cranes. The split-platform storage/retrieval system (SP-AS/RS) has been invented to store containers more efficiently and to access them more quickly. The integrated scheduling of quay cranes, automated guided vehicles and handling platforms in SP-AS/RS has been formulated and solved using the simulated annealing algorithm in previous literatures. This paper presents a genetic algorithm (GA) to solve this problem more accurately and precisely. The GA includes a new operator to make a random string of tasks observing the precedence relations between the tasks. For evaluating the performance of the GA, 10 small size test cases were solved by using the proposed GA and the results were compared to those from the literature. Results show that the proposed GA is able to find fairly near optimal solutions similar to the existing simulated annealing algorithm. Moreover, it is shown that the proposed GA outperforms the existing algorithm when the number of tasks in the scheduling horizon increases (e.g. 30 to 100).
format Article
author Homayouni, Seyed Mahdi
Tang, Sai Hong
Motlagh, Omid Reza Esmaeili
spellingShingle Homayouni, Seyed Mahdi
Tang, Sai Hong
Motlagh, Omid Reza Esmaeili
A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals
author_facet Homayouni, Seyed Mahdi
Tang, Sai Hong
Motlagh, Omid Reza Esmaeili
author_sort Homayouni, Seyed Mahdi
title A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals
title_short A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals
title_full A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals
title_fullStr A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals
title_full_unstemmed A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals
title_sort genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals
publisher Elsevier
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/36164/1/A%20genetic%20algorithm%20for%20optimization%20of%20integrated%20scheduling%20of%20cranes%2C%20vehicles%2C%20and%20storage%20platforms%20at%20automated%20container%20terminals.pdf
_version_ 1782721113906741248
score 12.933938

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