PUBLICATION DATE

1998-04-03

TITLE

Job shop scheduling with a genetic algorithm and machine learning

ISSUE

2

VOLUME

49

ISSN (print)

0020-580X

ISSN (electronic)

1476-9352

ABSTRACT

Dynamic job shop scheduling has been proven to be an intractable problem for analytical procedures. Recent advances in computing technology, especially in artificial intelligence, have alleviated this problem by intelligently restricting the search space considered, thus opening the possibility of obtaining better results. Researchers have used various techniques that were developed under the general rubric of artificial intelligence to solve job shop scheduling problems. The most common of these have been expert systems, genetic algorithms and machine learning. Of these, we identify machine learning and genetic algorithms to be promising for scheduling applications in a job shop. In this paper, we propose to combine complementarily the strengths of genetic algorithms and induced decision trees, a machine learning technique, to develop a job shop scheduling system. Empirical results, using machine learning for releasing jobs into the shop floor and a genetic algorithm to dispatch jobs at each machine, are promising.

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26 TRIPLES      24 PREDICATES      25 URIs      15 LITERALS

Subject Predicate Object
1 articles:04b72890da2442d0e25593d35a93d651 sg:abstract Dynamic job shop scheduling has been proven to be an intractable problem for analytical procedures. Recent advances in computing technology, especially in artificial intelligence, have alleviated this problem by intelligently restricting the search space considered, thus opening the possibility of obtaining better results. Researchers have used various techniques that were developed under the general rubric of artificial intelligence to solve job shop scheduling problems. The most common of these have been expert systems, genetic algorithms and machine learning. Of these, we identify machine learning and genetic algorithms to be promising for scheduling applications in a job shop. In this paper, we propose to combine complementarily the strengths of genetic algorithms and induced decision trees, a machine learning technique, to develop a job shop scheduling system. Empirical results, using machine learning for releasing jobs into the shop floor and a genetic algorithm to dispatch jobs at each machine, are promising.
2 sg:coverDate 1998-04-01
3 sg:coverYear 1998
4 sg:coverYearMonth 1998-04
5 sg:ddsIdJournalBrand iaor
6 sg:doi 10.1057/iaor.1998.747
7 sg:doiLink http://dx.doi.org/10.1057/iaor.1998.747
8 sg:hasArticleType article-types:research
9 sg:hasFieldOfResearchCode anzsrc-for:08
10 anzsrc-for:0801
11 sg:hasJournal journals:1587f0c23f6d790a0b249e0af78a213d
12 journals:d654b82ffa89697399434ee935ac5bbb
13 sg:hasJournalBrand journal-brands:11eaa1206191d0347361452c8e00709c
14 sg:issnElectronic 1476-9352
15 sg:issnPrint 0020-580X
16 sg:issue 2
17 sg:license http://scigraph.springernature.com/explorer/license/
18 sg:npgId iaor1998747
19 sg:publicationDate 1998-04-03
20 sg:publicationYear 1998
21 sg:publicationYearMonth 1998-04
22 sg:scigraphId 04b72890da2442d0e25593d35a93d651
23 sg:title Job shop scheduling with a genetic algorithm and machine learning
24 sg:volume 49
25 rdf:type sg:Article
26 rdfs:label Article: Job shop scheduling with a genetic algorithm and machine learning
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