A Genetic Algorithm Approach for Scheduling Trains Maintenance Under Uncertainty

Gu, H; Lam, HC

A Genetic Algorithm Approach for Scheduling Trains Maintenance Under Uncertainty

Gu, H

Lam, HC

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2020, 1121 AISC pp. 106 - 118
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583	en_US
dc.contributor.author	Lam, HC	en_US
dc.date.available	2020-12-19T18:05:21Z
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2020, 1121 AISC pp. 106 - 118	en_US
dc.identifier.isbn	9783030383633	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137888
dc.description.abstract	© Springer Nature Switzerland AG 2020. This paper investigates the overhaul maintenance scheduling problem in which the maintenance duration is uncertain at the time of planning. This problem involves specifying the dates of trains’ arrival at the maintenance centre while taking into consideration the due windows, the desired number of trains in service, and the capacity of the maintenance centre. The cycle time of each type of trains is random with a known probability distribution. The objective is to minimise a weighted sum of two components: (i) the deviation of the assigned arrival dates from the due windows and (ii) the penalty for violating the resources’ constraints. A combined genetic algorithm with sample average approximation solution approach is developed to solve this problem. The solution approach consists of a genetic algorithm for global search and an exact method to determine the arrival dates of train-sets when a sequence of train-sets is known. The results with data provided by one of the leading Australian maintenance center show that the proposed method can produce good solution within acceptable computation time.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-030-38364-0_10	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Genetic Algorithm Approach for Scheduling Trains Maintenance Under Uncertainty	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1121 AISC	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	1121 AISC	en_US

Abstract:

© Springer Nature Switzerland AG 2020. This paper investigates the overhaul maintenance scheduling problem in which the maintenance duration is uncertain at the time of planning. This problem involves specifying the dates of trains’ arrival at the maintenance centre while taking into consideration the due windows, the desired number of trains in service, and the capacity of the maintenance centre. The cycle time of each type of trains is random with a known probability distribution. The objective is to minimise a weighted sum of two components: (i) the deviation of the assigned arrival dates from the due windows and (ii) the penalty for violating the resources’ constraints. A combined genetic algorithm with sample average approximation solution approach is developed to solve this problem. The solution approach consists of a genetic algorithm for global search and an exact method to determine the arrival dates of train-sets when a sequence of train-sets is known. The results with data provided by one of the leading Australian maintenance center show that the proposed method can produce good solution within acceptable computation time.

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http://hdl.handle.net/10453/137888