Hybrid evolutionary computation methods for quay crane scheduling problems
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gp-bibliography.bib Revision:1.7954
- @Article{Nguyen:2013:COR,
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author = "Su Nguyen and Mengjie Zhang and Mark Johnston and
Kay Chen Tan",
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title = "Hybrid evolutionary computation methods for quay crane
scheduling problems",
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journal = "Computer \& Operations Research",
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volume = "40",
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number = "8",
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pages = "2083--2093",
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year = "2013",
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keywords = "genetic algorithms, genetic programming, Local search,
Quay crane scheduling",
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ISSN = "0305-0548",
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DOI = "doi:10.1016/j.cor.2013.03.007",
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URL = "http://www.sciencedirect.com/science/article/pii/S030505481300083X",
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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