PUBLICATION DATE

2013-04-22

TITLE

Single‐machine and two‐machine flowshop scheduling problems with truncated position‐based learning functions

ISSUE

4

VOLUME

64

ISSN (print)

0020-580X

ISSN (electronic)

1476-9352

ABSTRACT

Scheduling with learning effects has received growing attention nowadays. A well‐known learning model is called ‘position‐based learning’ in which the actual processing time of a job is a non‐increasing function of its position to be processed. However, the actual processing time of a given job drops to zero precipitously as the number of jobs increases. Motivated by this observation, we propose two truncated learning models in single‐machine scheduling problems and two‐machine flowshop scheduling problems with ordered job processing times, respectively, where the actual processing time of a job is a function of its position and a control parameter. Under the proposed learning models, we show that some scheduling problems can be solved in polynomial time. In addition, we further analyse the worst‐case error bounds for the problems to minimize the total weighted completion time, discounted total weighted completion time and maximum lateness.

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25 TRIPLES      23 PREDICATES      25 URIs      15 LITERALS

Subject Predicate Object
1 articles:42811432fb769153a69bbb16589e8630 sg:abstract Scheduling with learning effects has received growing attention nowadays. A well‐known learning model is called ‘position‐based learning’ in which the actual processing time of a job is a non‐increasing function of its position to be processed. However, the actual processing time of a given job drops to zero precipitously as the number of jobs increases. Motivated by this observation, we propose two truncated learning models in single‐machine scheduling problems and two‐machine flowshop scheduling problems with ordered job processing times, respectively, where the actual processing time of a job is a function of its position and a control parameter. Under the proposed learning models, we show that some scheduling problems can be solved in polynomial time. In addition, we further analyse the worst‐case error bounds for the problems to minimize the total weighted completion time, discounted total weighted completion time and maximum lateness.
2 sg:ddsIdJournalBrand iaor
3 sg:doi 10.1057/iaor.2012.43912
4 sg:doiLink http://dx.doi.org/10.1057/iaor.2012.43912
5 sg:hasArticleType article-types:research
6 sg:hasFieldOfResearchCode anzsrc-for:17
7 anzsrc-for:1701
8 sg:hasJournal journals:1587f0c23f6d790a0b249e0af78a213d
9 journals:d654b82ffa89697399434ee935ac5bbb
10 sg:hasJournalBrand journal-brands:11eaa1206191d0347361452c8e00709c
11 sg:issnElectronic 1476-9352
12 sg:issnPrint 0020-580X
13 sg:issue 4
14 sg:license http://scigraph.springernature.com/explorer/license/
15 sg:npgId iaor20128162
16 sg:pageEnd
17 sg:pageStart
18 sg:publicationDate 2013-04-22
19 sg:publicationYear 2013
20 sg:publicationYearMonth 2013-04
21 sg:scigraphId 42811432fb769153a69bbb16589e8630
22 sg:title Single‐machine and two‐machine flowshop scheduling problems with truncated position‐based learning functions
23 sg:volume 64
24 rdf:type sg:Article
25 rdfs:label Article: Single‐machine and two‐machine flowshop scheduling problems with truncated position‐based learning functions
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