Evolving dispatching rules for dynamic Job shop scheduling with uncertain processing times

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

@InProceedings{karunakaran:2017:CEC,
  author =       "Deepa Karunakaran and Yi Mei and Gang Chen and 
                 Mengjie Zhang",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Evolving dispatching rules for dynamic Job shop
                 scheduling with uncertain processing times",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "364--371",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Dynamic Job shop scheduling (DJSS) is a complex and
                 hard problem in real-world manufacturing systems. In
                 practice, the parameters of a job shop like processing
                 times, due dates, etc. are uncertain. But most of the
                 current research on scheduling consider only
                 deterministic scenarios. In a typical dynamic job shop,
                 once the information about a job becomes available it
                 is considered unchanged. In this work, we consider
                 genetic programming based dispatching rules to generate
                 schedules in an uncertain environment where the process
                 time of an operation is not known exactly until it is
                 finished. Our primary goal is to investigate methods to
                 incorporate the uncertainty information into the
                 dispatching rules. We develop two training approaches,
                 namely ex-post and ex-ante to evolve the dispatching
                 rules to generate good schedules under uncertainty.
                 Both these methods consider different ways of
                 incorporating the uncertainty parameters into the
                 genetic programs during evolution. We test our methods
                 under different scenarios and the results compare well
                 against the existing approaches. We also test the
                 generalization capability of our methods across
                 different levels of uncertainty and observe that the
                 proposed methods perform well. In particular, we
                 observe that the proposed ex-ante training approach
                 outperformed other methods.",
  keywords =     "genetic algorithms, genetic programming, dispatching,
                 job shop scheduling, manufacturing systems, dynamic job
                 shop scheduling, ex-ante training, ex-post training,
                 generalization capability, genetic programming based
                 dispatching rules, training approaches, uncertain
                 environment, uncertainty information, uncertainty
                 parameters, Dynamic scheduling, Optimization,
                 Schedules, Training, Uncertainty",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969335",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969335}",
}

Genetic Programming entries for Deepa Karunakaran Yi Mei Gang Chen Mengjie Zhang

Citations