Dynamic Multi-objective Job Shop Scheduling: A Genetic Programming Approach

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

@InCollection{Nguyen:2013:asp,
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "Dynamic Multi-objective Job Shop Scheduling: A Genetic
                 Programming Approach",
  booktitle =    "Automated Scheduling and Planning",
  publisher =    "Springer",
  year =         "2013",
  editor =       "A. Sima Uyar and Ender Ozcan and Neil Urquhart",
  volume =       "505",
  series =       "Studies in Computational Intelligence",
  pages =        "251--282",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-39303-7",
  DOI =          "doi:10.1007/978-3-642-39304-4_10",
  abstract =     "Handling multiple conflicting objectives in dynamic
                 job shop scheduling is challenging because many aspects
                 of the problem need to be considered when designing
                 dispatching rules. A multi-objective genetic
                 programming based hyperheuristic (MO-GPHH) method is
                 investigated here to facilitate the designing task. The
                 goal of this method is to evolve a Pareto front of
                 non-dominated dispatching rules which can be used to
                 support the decision makers by providing them with
                 potential trade-offs among different objectives. The
                 experimental results under different shop conditions
                 suggest that the evolved Pareto front contains very
                 effective rules. Some extensive analyses are also
                 presented to help confirm the quality of the evolved
                 rules. The Pareto front obtained can cover a much wider
                 ranges of rules as compared to a large number of
                 dispatching rules reported in the literature. Moreover,
                 it is also shown that the evolved rules are robust
                 across different shop conditions.",
}

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston Kay Chen Tan

Citations