Sampling Heuristics for Multi-Objective Dynamic Job Shop Scheduling Using Island Based Parallel Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{Karunakaran:2018:PPSN,
  author =       "Deepak Karunakaran and Yi Mei and Gang Chen and 
                 Mengjie Zhang",
  title =        "Sampling Heuristics for Multi-Objective Dynamic Job
                 Shop Scheduling Using Island Based Parallel Genetic
                 Programming",
  booktitle =    "15th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2018",
  editor =       "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and 
                 Penousal Machado and Luis Paquete and Darrell Whitley",
  volume =       "11102",
  series =       "LNCS",
  pages =        "347--359",
  address =      "Coimbra, Portugal",
  month =        "8-12 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Scheduling,
                 Parallel algorithms",
  isbn13 =       "978-3-319-99258-7",
  URL =          "https://www.springer.com/gp/book/9783319992587",
  DOI =          "doi:10.1007/978-3-319-99259-4_28",
  abstract =     "Dynamic job shop scheduling is a complex problem in
                 production systems. Automated design of dispatching
                 rules for these systems, particularly using the genetic
                 programming based hyper-heuristics (GPHH) has been a
                 promising approach in recent years. However, GPHH is a
                 computationally intensive and time consuming approach.
                 Parallel evolutionary algorithms are one of the key
                 approaches to tackle this drawback. Furthermore when
                 scheduling is performed under uncertain manufacturing
                 environments while considering multiple conflicting
                 objectives, evolving good rules requires large and
                 diverse training instances. Under limited time and
                 computational budget training on all instances is not
                 possible. Therefore, we need an efficient way to decide
                 which training samples are more suitable for training.
                 We propose a method to sample those problem instances
                 which have the potential to promote the evolution of
                 good rules. In particular, a sampling heuristic which
                 successively rejects clusters of problem instances in
                 favour of those problem instances which show potential
                 in improving the Pareto front for a dynamic
                 multi-objective scheduling problem is developed. We
                 exploit the efficient island model-based approaches to
                 simultaneously consider multiple training instances for
                 GPHH.",
  notes =        "PPSN2018 http://ppsn2018.dei.uc.pt

                 This two-volume set LNCS 11101 and 11102 constitutes
                 the refereed proceedings of the 15th International
                 Conference on Parallel Problem Solving from Nature,
                 PPSN 2018",
}

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

Citations