Scheduling batch processing in flexible flowshop with job dependent buffer requirements: Lagrangian relaxation approach

Gu, H; Memar, J; Zinder, Y

Scheduling batch processing in flexible flowshop with job dependent buffer requirements: Lagrangian relaxation approach

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, 10755 LNCS pp. 119 - 131
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, 10755 LNCS pp. 119 - 131	en_US
dc.identifier.isbn	9783319751719	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132424
dc.description.abstract	© Springer International Publishing AG, part of Springer Nature 2018. The paper presents a Lagrangian relaxation based algorithm for scheduling jobs in the two-stage flowshop where the first stage is comprised of several parallel identical machines and the second stage consists of a single machine processing jobs in the predefined batches. Motivated by applications where unloading and loading occur when the means of transportation are changed, the processing of the jobs, constituting a batch, can commence only if this batch has been allocated a portion of a limited buffer associated with the flowshop. This portion varies from batch to batch and is released only after the completion of the batch processing on the second stage machine. Each batch has a due date and the objective is to minimise the total weighted tardiness. The effectiveness of the proposed algorithm is demonstrated by 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	10.1007/978-3-319-75172-6_11	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Scheduling batch processing in flexible flowshop with job dependent buffer requirements: Lagrangian relaxation approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10755 LNCS	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	10755 LNCS	en_US

Abstract:

© Springer International Publishing AG, part of Springer Nature 2018. The paper presents a Lagrangian relaxation based algorithm for scheduling jobs in the two-stage flowshop where the first stage is comprised of several parallel identical machines and the second stage consists of a single machine processing jobs in the predefined batches. Motivated by applications where unloading and loading occur when the means of transportation are changed, the processing of the jobs, constituting a batch, can commence only if this batch has been allocated a portion of a limited buffer associated with the flowshop. This portion varies from batch to batch and is released only after the completion of the batch processing on the second stage machine. Each batch has a due date and the objective is to minimise the total weighted tardiness. The effectiveness of the proposed algorithm is demonstrated by computational experiments.

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http://hdl.handle.net/10453/132424