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

Gu, H; Kononov, A; Memar, J; Zinder, Y

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

Gu, H

Kononov, A

Memar, J

Zinder, Y

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Publication Type:: Journal Article
Citation:: Journal of Discrete Algorithms, 2018, 52-53 pp. 143 - 155
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583	en_US
dc.contributor.author	Kononov, A https://orcid.org/0000-0001-6144-0251	en_US
dc.contributor.author	Memar, J https://orcid.org/0000-0003-2395-1450	en_US
dc.contributor.author	Zinder, Y	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	Journal of Discrete Algorithms, 2018, 52-53 pp. 143 - 155	en_US
dc.identifier.issn	1570-8667	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130281
dc.description.abstract	© 2018 Elsevier B.V. The paper is concerned with minimisation of the 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 in the strong sense, and we prove that if the order of jobs is fixed for one of the stages, then even for the criteria of the maximum completion time or the total completion time the problem remains NP-hard in the strong sense. 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	Journal of Discrete Algorithms	en_US
dc.relation.isbasedon	10.1016/j.jda.2018.11.011	en_US
dc.subject.classification	Computation Theory & Mathematics	en_US
dc.title	Efficient Lagrangian heuristics for the two-stage flow shop with job dependent buffer requirements	en_US
dc.type	Journal Article
utslib.citation.volume	52-53	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0802 Computation Theory and 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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	52-53	en_US

Abstract:

© 2018 Elsevier B.V. The paper is concerned with minimisation of the 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 in the strong sense, and we prove that if the order of jobs is fixed for one of the stages, then even for the criteria of the maximum completion time or the total completion time the problem remains NP-hard in the strong sense. 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/10453/130281