Evolving "less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming

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

  author =       "Rachel Hunt and Mark Johnston and Mengjie Zhang",
  title =        "Evolving {"}less-myopic{"} scheduling rules for
                 dynamic job shop scheduling with genetic programming",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "927--934",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598224",
  DOI =          "doi:10.1145/2576768.2598224",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Job Shop Scheduling (JSS) is a complex real-world
                 problem aiming to optimise a measure of delivery speed
                 or customer satisfaction by determining a schedule for
                 processing jobs on machines. A major disadvantage of
                 using a dispatching rule (DR) approach to solving JSS
                 problems is their lack of a global perspective of the
                 current and potential future state of the shop. We
                 investigate a genetic programming based hyper-heuristic
                 (GPHH) approach to develop less-myopic DRs for dynamic
                 JSS. Results show that in the dynamic ten machine job
                 shop, incorporating features of the state of the wider
                 shop, and the stage of a job's journey through the
                 shop, improves the mean performance, and decreases the
                 standard deviation of performance of the best evolved
  notes =        "Also known as \cite{2598224} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",

Genetic Programming entries for Rachel Hunt Mark Johnston Mengjie Zhang