Evolving Stochastic Dispatching Rules for Order Acceptance and Scheduling via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Park:2013:AI,
  author =       "John Park and Su Nguyen and Mark Johnston and 
                 Mengjie Zhang",
  title =        "Evolving Stochastic Dispatching Rules for Order
                 Acceptance and Scheduling via Genetic Programming",
  booktitle =    "Proceedings of the 26th Australasian Joint Conference
                 on Artificial Intelligence (AI2013)",
  year =         "2013",
  editor =       "Stephen Cranefield and Abhaya Nayak",
  volume =       "8272",
  series =       "LNAI",
  pages =        "478--489",
  address =      "Dunedin, New Zealand",
  month =        "1-6 " # dec,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-03679-3",
  URL =          "http://dx.doi.org/10.1007/978-3-319-03680-9_48",
  DOI =          "doi:10.1007/978-3-319-03680-9_48",
  size =         "12 pages",
  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.",
  notes =        "Hyper Heuristic?",
}

Genetic Programming entries for John Park Su Nguyen Mark Johnston Mengjie Zhang

Citations