PUBLICATION DATE

2012-04-18

AUTHORS

C-C Wu, S-R Cheng, Y Yin

TITLE

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

ISSUE

1

VOLUME

64

ISSN (print)

0160-5682

ISSN (electronic)

1476-9360

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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35 TRIPLES      28 PREDICATES      36 URIs      20 LITERALS

Subject Predicate Object
1 articles:5a2d208786289d55de62e44f909637e6 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:coverYear 2013
3 sg:coverYearMonth 2013-01
4 sg:ddsIdJournalBrand 41274
5 sg:doi 10.1057/jors.2012.46
6 sg:doiLink http://dx.doi.org/10.1057/jors.2012.46
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21 sg:issnElectronic 1476-9360
22 sg:issnPrint 0160-5682
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25 sg:npgId jors201246
26 sg:pageEnd 156
27 sg:pageStart 147
28 sg:publicationDate 2012-04-18
29 sg:publicationYear 2012
30 sg:publicationYearMonth 2012-04
31 sg:scigraphId 5a2d208786289d55de62e44f909637e6
32 sg:title Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions
33 sg:volume 64
34 rdf:type sg:Article
35 rdfs:label Article: Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions
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