Evolving Stochastic Dispatching Rules for Order Acceptance and Scheduling via Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Park:2013:AI,
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author = "John Park and Su Nguyen and Mark Johnston and
Mengjie Zhang",
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title = "Evolving Stochastic Dispatching Rules for Order
Acceptance and Scheduling via Genetic Programming",
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booktitle = "Proceedings of the 26th Australasian Joint Conference
on Artificial Intelligence (AI2013)",
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year = "2013",
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editor = "Stephen Cranefield and Abhaya Nayak",
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volume = "8272",
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series = "LNAI",
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pages = "478--489",
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address = "Dunedin, New Zealand",
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month = "1-6 " # dec,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-03679-3",
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URL = "http://dx.doi.org/10.1007/978-3-319-03680-9_48",
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DOI = "doi:10.1007/978-3-319-03680-9_48",
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size = "12 pages",
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abstract = "This paper focuses on Order Acceptance and Scheduling
(OAS) problems in make-to-order manufacturing systems,
which handle both acceptance and sequencing decisions
simultaneously to maximise the total revenue. Since OAS
is a NP-hard problem, several heuristics and
meta-heuristics have been proposed to find near-optimal
solutions in reasonable computational times. However,
previous approaches still have trouble dealing with
complex cases in OAS and they often need to be manually
customised to handle specific OAS problems. Developing
effective and efficient heuristics for OAS is a
difficult task. In order to facilitate the development
process, this paper proposes a new genetic programming
(GP) method to automatically generate dispatching rules
to solve OAS problems. To improve the effectiveness of
evolved rules, the proposed GP method incorporates
stochastic behaviours into dispatching rules to help
explore multiple potential solutions effectively. The
experimental results show that evolved stochastic
dispatching rules (SDRs) can outperform the tabu search
heuristic especially customised for OAS. In addition,
the evolved SDRs also show better results as compared
to rules evolved by the simple GP method.",
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notes = "Hyper Heuristic?",
- }
Genetic Programming entries for
John Park
Su Nguyen
Mark Johnston
Mengjie Zhang
Citations