The just-in-time job-shop scheduling problem with distinct due-dates for operations

Ahmadian, MM; Salehipour, A

The just-in-time job-shop scheduling problem with distinct due-dates for operations

Ahmadian, MM Salehipour, A

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of Heuristics, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Ahmadian, MM
dc.contributor.author	Salehipour, A
dc.date.accessioned	2021-01-08T03:18:19Z
dc.date.available	2021-01-08T03:18:19Z
dc.date.issued	2020-01-01
dc.identifier.citation	Journal of Heuristics, 2020
dc.identifier.issn	1381-1231
dc.identifier.issn	1572-9397
dc.identifier.uri	http://hdl.handle.net/10453/145216
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. In the just-in-time job-shop scheduling (JIT–JSS) problem every operation has a distinct due-date, and earliness and tardiness penalties. Any deviation from the due-date incurs penalties. The objective of JIT–JSS is to obtain a schedule, i.e., the completion time for performing the operations, with the smallest total (weighted) earliness and tardiness penalties. This paper presents a matheuristic algorithm for the JIT–JSS problem, which operates by decomposing the problem into smaller sub-problems, optimizing the sub-problems and delivering the optimal schedule for the problem. By solving a set of 72 benchmark instances ranging from 10 to 20 jobs and 20 to 200 operations we show that the proposed algorithm outperforms the state-of-the-art methods and the solver CPLEX, and obtains new best solutions for nearly 56% of the instances, including for 79% of the large instances with 20 jobs.
dc.language	English
dc.publisher	SPRINGER
dc.relation	http://purl.org/au-research/grants/arc/DE170100234
dc.relation.ispartof	Journal of Heuristics
dc.relation.isbasedon	10.1007/s10732-020-09458-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.subject.classification	Operations Research
dc.title	The just-in-time job-shop scheduling problem with distinct due-dates for operations
dc.type	Journal Article
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-08T03:18:04Z
pubs.publication-status	Published

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. In the just-in-time job-shop scheduling (JIT–JSS) problem every operation has a distinct due-date, and earliness and tardiness penalties. Any deviation from the due-date incurs penalties. The objective of JIT–JSS is to obtain a schedule, i.e., the completion time for performing the operations, with the smallest total (weighted) earliness and tardiness penalties. This paper presents a matheuristic algorithm for the JIT–JSS problem, which operates by decomposing the problem into smaller sub-problems, optimizing the sub-problems and delivering the optimal schedule for the problem. By solving a set of 72 benchmark instances ranging from 10 to 20 jobs and 20 to 200 operations we show that the proposed algorithm outperforms the state-of-the-art methods and the solver CPLEX, and obtains new best solutions for nearly 56% of the instances, including for 79% of the large instances with 20 jobs.

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http://hdl.handle.net/10453/145216