Hybrid evolutionary computation methods for quay crane scheduling problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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