A hybrid genetic algorithm for scheduling jobs sharing multiple resources under uncertainty

Gu, H; Lam, HC; Zinder, Y

A hybrid genetic algorithm for scheduling jobs sharing multiple resources under uncertainty

Gu, H

Lam, HC Zinder, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: EURO Journal on Computational Optimization, 2022, 10, pp. 100050
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583
dc.contributor.author	Lam, HC
dc.contributor.author	Zinder, Y https://orcid.org/0000-0003-2024-8129
dc.date.accessioned	2023-01-31T01:52:48Z
dc.date.available	2023-01-31T01:52:48Z
dc.date.issued	2022-01-01
dc.identifier.citation	EURO Journal on Computational Optimization, 2022, 10, pp. 100050
dc.identifier.issn	2192-4406
dc.identifier.issn	2192-4414
dc.identifier.uri	http://hdl.handle.net/10453/165641
dc.description.abstract	This study addresses the scheduling problem where every job requires several types of resources. At every point in time, the capacity of resources is limited. When necessary, the capacity can be increased at a cost. Each job has a due date, and the processing times of jobs are random variables with a known probability distribution. The considered problem is to determine a schedule that minimises the total cost, which consists of the cost incurred due to the violation of resource limits and the total tardiness of jobs. A genetic algorithm enhanced by local search is proposed. The sample average approximation method is used to construct a confidence interval for the optimality gap of the obtained solutions. Computational study on the application of the sample average approximation method and genetic algorithm is presented. It is revealed that the proposed method is capable of providing high-quality solutions to large instances in a reasonable time.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	EURO Journal on Computational Optimization
dc.relation.isbasedon	10.1016/j.ejco.2022.100050
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A hybrid genetic algorithm for scheduling jobs sharing multiple resources under uncertainty
dc.type	Journal Article
utslib.citation.volume	10
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	*
dc.date.updated	2023-01-31T01:52:27Z
pubs.publication-status	Published
pubs.volume	10

Abstract:

This study addresses the scheduling problem where every job requires several types of resources. At every point in time, the capacity of resources is limited. When necessary, the capacity can be increased at a cost. Each job has a due date, and the processing times of jobs are random variables with a known probability distribution. The considered problem is to determine a schedule that minimises the total cost, which consists of the cost incurred due to the violation of resource limits and the total tardiness of jobs. A genetic algorithm enhanced by local search is proposed. The sample average approximation method is used to construct a confidence interval for the optimality gap of the obtained solutions. Computational study on the application of the sample average approximation method and genetic algorithm is presented. It is revealed that the proposed method is capable of providing high-quality solutions to large instances in a reasonable time.

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http://hdl.handle.net/10453/165641