Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.4163

@InProceedings{Park:2018:EuroGP,
  author =       "John Park and Yi Mei and Su Nguyen and Gang Chen2 and 
                 Mengjie Zhang",
  title =        "Investigating a Machine Breakdown Genetic Programming
                 Approach for Dynamic Job Shop Scheduling",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "253--270",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming: Poster",
  isbn13 =       "978-3-319-77552-4",
  URL =          "http://homepages.ecs.vuw.ac.nz/~yimei/papers/EuroGP18-John.pdf",
  DOI =          "doi:10.1007/978-3-319-77553-1_16",
  abstract =     "Dynamic job shop scheduling (JSS) problems with
                 dynamic job arrivals have been studied extensively in
                 the literature due to their applicability to real-world
                 manufacturing systems, such as semiconductor
                 manufacturing. In a dynamic JSS problem with dynamic
                 job arrivals, jobs arrive on the shop floor unannounced
                 that need to be processed by the machines on the shop
                 floor. A job has a sequence of operations that can only
                 processed on specific machines, and machines can only
                 process one job at a time. Many effective genetic
                 programming based hyper-heuristic (GP-HH) approaches
                 have been proposed for dynamic JSS problems with
                 dynamic job arrivals, where high quality dispatching
                 rules are automatically evolved by GP to handle the
                 dynamic JSS problem instances. However, research that
                 focus on handling multiple dynamic events
                 simultaneously are limited, such as both dynamic job
                 arrivals and machine breakdowns. A machine breakdown
                 event results in the affected machine being unable to
                 process any jobs during the repair time. It is likely
                 that machine breakdowns can significantly affect the
                 effectiveness of the scheduling procedure unless they
                 are explicitly accounted for. Therefore, this paper
                 develops new machine breakdown terminals for a GP
                 approach and evaluates their effectiveness for a
                 dynamic JSS problem with both dynamic job arrivals and
                 machine breakdowns. The results show that the GP
                 approaches with the machine breakdown terminals do show
                 improvements. The analysis shows that the machine
                 breakdown terminals may indirectly contribute in the
                 evolution of high quality rules, but occur infrequently
                 in the output rules evolved by the machine breakdown GP
                 approaches.",
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and
                 EvoApplications2018",
}

Genetic Programming entries for John Park Yi Mei Su Nguyen Gang Chen2 Mengjie Zhang

Citations