Toward Evolving Dispatching Rules for Dynamic Job Shop Scheduling Under Uncertainty

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Deepak Karunakaran and Yi Mei and Gang Chen2 and 
                 Mengjie Zhang",
  title =        "Toward Evolving Dispatching Rules for Dynamic Job Shop
                 Scheduling Under Uncertainty",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "282--289",
  size =         "8 pages",
  URL =          "",
  DOI =          "doi:10.1145/3071178.3071202",
  acmid =        "3071202",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, job shop
                 scheduling, uncertainty",
  month =        "15-19 " # jul,
  abstract =     "Dynamic job shop scheduling (DJSS) is a complex
                 problem which is an important aspect of manufacturing
                 systems. Even though the manufacturing environment is
                 uncertain, most of the existing research works consider
                 deterministic scheduling problems where the time
                 required for processing any job is known in advance and
                 never changes. In this work, we consider DJSS problems
                 with varied uncertainty configurations of machines in
                 terms of processing times and the total flow time as
                 scheduling objective. With the varying levels of
                 uncertainty many machines become bottlenecks of the job
                 shop. It is essential to identify these bottleneck
                 machines and schedule the jobs to be performed by them
                 carefully. Driven by this idea, we develop a new
                 effective method to evolve pairs of dispatching rules
                 each for a different bottleneck level of the machines.
                 A clustering approach to classifying the bottleneck
                 level of the machines arising in the system due to
                 uncertain processing times is proposed. Then, a
                 cooperative co-evolution technique to evolve pairs of
                 dispatching rules which generalize well across
                 different uncertainty configurations is presented. We
                 perform empirical analysis to show its generalization
                 characteristic over the different uncertainty
                 configurations and show that the proposed method
                 outperforms the current approaches.",
  notes =        "Also known as
                 \cite{Karunakaran:2017:TED:3071178.3071202} GECCO-2017
                 A Recombination of the 26th International Conference on
                 Genetic Algorithms (ICGA-2017) and the 22nd Annual
                 Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Deepak Karunakaran Yi Mei Gang Chen2 Mengjie Zhang