1. ScholarBank@NUS
  2. 1. Staff
  3. Staff Publications
Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-29139-5_11
DC FieldValue
dc.titleEvolving reusable operation-based due-date assignment models for job shop scheduling with genetic programming
dc.contributor.authorNguyen, S.
dc.contributor.authorZhang, M.
dc.contributor.authorJohnston, M.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-06-19T03:09:42Z
dc.date.available2014-06-19T03:09:42Z
dc.date.issued2012
dc.identifier.citationNguyen, S.,Zhang, M.,Johnston, M.,Tan, K.C. (2012). Evolving reusable operation-based due-date assignment models for job shop scheduling with genetic programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7244 LNCS : 121-133. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-29139-5_11" target="_blank">https://doi.org/10.1007/978-3-642-29139-5_11</a>
dc.identifier.isbn9783642291388
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/70228
dc.description.abstractDue-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic features of job shops, the development of general due-date assignment models (DDAMs) is complicated. In this study, two genetic programming (GP) methods are proposed to evolve DDAMs for job shop environments. The experimental results show that the evolved DDAMs can make more accurate estimates than other existing dynamic DDAMs with promising reusability. In addition, the evolved operation-based DDAMs show better performance than the evolved DDAMs employing aggregate information of jobs and machines. © 2012 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-29139-5_11
dc.sourceScopus
dc.subjectDue-date assignment
dc.subjectGenetic programming
dc.subjectJob shop
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-29139-5_11
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7244 LNCS
dc.description.page121-133
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.