A lagrangian relaxation-based heuristic to solve large extended graph partitioning problems

Czibula, OG; Gu, H; Zinder, Y

A lagrangian relaxation-based heuristic to solve large extended graph partitioning problems

Czibula, OG

Gu, H

Zinder, Y

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9627 pp. 327 - 338
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Czibula, OG https://orcid.org/0000-0002-9788-9189	en_US
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583	en_US
dc.contributor.author	Zinder, Y	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9627 pp. 327 - 338	en_US
dc.identifier.isbn	9783319301389	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/98958
dc.description.abstract	© Springer International Publishing Switzerland 2016. The paper is concerned with the planning of training sessions in large organisations requiring periodic retraining of their staff. The allocation of students must take into account student preferences as well as the desired composition of study groups. The paper presents a bicriteria Quadratic Multiple Knapsack formulation of the considered practical problem, and a novel solution procedure based on Lagrangian relaxation. The paper presents the results of computational experiments aimed at testing the optimisation procedure on real world data originating from Australia’s largest electricity distributor. Results are compared and validated against a Genetic Algorithm based matheuristic.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-30139-6_26	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A lagrangian relaxation-based heuristic to solve large extended graph partitioning problems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9627	en_US
utslib.for	0102 Applied Mathematics	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	9627	en_US

Abstract:

© Springer International Publishing Switzerland 2016. The paper is concerned with the planning of training sessions in large organisations requiring periodic retraining of their staff. The allocation of students must take into account student preferences as well as the desired composition of study groups. The paper presents a bicriteria Quadratic Multiple Knapsack formulation of the considered practical problem, and a novel solution procedure based on Lagrangian relaxation. The paper presents the results of computational experiments aimed at testing the optimisation procedure on real world data originating from Australia’s largest electricity distributor. Results are compared and validated against a Genetic Algorithm based matheuristic.

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http://hdl.handle.net/10453/98958