Hybrid evolutionary computation methods for quay crane scheduling problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Nguyen:2013:COR,
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "Hybrid evolutionary computation methods for quay crane
                 scheduling problems",
  journal =      "Computer \& Operations Research",
  volume =       "40",
  number =       "8",
  pages =        "2083--2093",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, Local search,
                 Quay crane scheduling",
  ISSN =         "0305-0548",
  DOI =          "doi:10.1016/j.cor.2013.03.007",
  URL =          "http://www.sciencedirect.com/science/article/pii/S030505481300083X",
  abstract =     "Quay crane scheduling is one of the most important
                 operations in seaport terminals. The effectiveness of
                 this operation can directly influence the overall
                 performance as well as the competitive advantages of
                 the terminal. This paper develops a new priority-based
                 schedule construction procedure to generate quay crane
                 schedules. From this procedure, two new hybrid
                 evolutionary computation methods based on genetic
                 algorithm (GA) and genetic programming (GP) are
                 developed. The key difference between the two methods
                 is their representations which decide how priorities of
                 tasks are determined. While GA employs a permutation
                 representation to decide the priorities of tasks, GP
                 represents its individuals as a priority function which
                 is used to calculate the priorities of tasks. A local
                 search heuristic is also proposed to improve the
                 quality of solutions obtained by GA and GP. The
                 proposed hybrid evolutionary computation methods are
                 tested on a large set of benchmark instances and the
                 computational results show that they are competitive
                 and efficient as compared to the existing methods. Many
                 new best known solutions for the benchmark instances
                 are discovered by using these methods. In addition, the
                 proposed methods also show their flexibility when
                 applied to generate robust solutions for quay crane
                 scheduling problems under uncertainty. The results show
                 that the obtained robust solutions are better than
                 those obtained from the deterministic inputs.",
}

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

Citations