Efficient lagrangian heuristics for the two-stage flow shop with job dependent buffer requirements

Gu, H; Memar, J; Zinder, Y

Efficient lagrangian heuristics for the two-stage flow shop with job dependent buffer requirements

Gu, H

Memar, J

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), 2018, 10765 LNCS pp. 312 - 324
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583	en_US
dc.contributor.author	Memar, J https://orcid.org/0000-0003-2395-1450	en_US
dc.contributor.author	Zinder, Y https://orcid.org/0000-0003-2024-8129	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10765 LNCS pp. 312 - 324	en_US
dc.identifier.isbn	9783319788241	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137695
dc.description.abstract	© Springer International Publishing AG, part of Springer Nature 2018. The paper is concerned with minimisation of total weighted completion time for the two-stage flow shop with a buffer. In contrast to the vast literature on this topic, the buffer requirement varies from job to job and a job occupies the buffer continuously from the start of its first operation till the completion of its second operation rather than only between operations. Such problems arise in supply chains requiring unloading and loading of minerals and in some multimedia systems. The problem is NP-hard and the straightforward integer programming approach is impossible even for modest problem sizes. The paper presents a Lagrangian relaxation based decomposition approach that allows to use for each problem, obtained by this decomposition, a very fast algorithm. Several Lagrangian heuristics are evaluated by means of computational experiments.	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	10.1007/978-3-319-78825-8_26	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Efficient lagrangian heuristics for the two-stage flow shop with job dependent buffer requirements	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10765 LNCS	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	010206 Operations Research	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	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10765 LNCS	en_US

Abstract:

© Springer International Publishing AG, part of Springer Nature 2018. The paper is concerned with minimisation of total weighted completion time for the two-stage flow shop with a buffer. In contrast to the vast literature on this topic, the buffer requirement varies from job to job and a job occupies the buffer continuously from the start of its first operation till the completion of its second operation rather than only between operations. Such problems arise in supply chains requiring unloading and loading of minerals and in some multimedia systems. The problem is NP-hard and the straightforward integer programming approach is impossible even for modest problem sizes. The paper presents a Lagrangian relaxation based decomposition approach that allows to use for each problem, obtained by this decomposition, a very fast algorithm. Several Lagrangian heuristics are evaluated by means of computational experiments.

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http://hdl.handle.net/10453/137695