A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Lin:2020:ESA,
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author = "Jian Lin and Lei Zhu and Kaizhou Gao",
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title = "A genetic programming hyper-heuristic approach for the
multi-skill resource constrained project scheduling
problem",
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journal = "Expert Systems with Applications",
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year = "2020",
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volume = "140",
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pages = "112915",
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month = feb,
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keywords = "genetic algorithms, genetic programming,
Hyper-heuristic, Multi-skill, Project scheduling",
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ISSN = "0957-4174",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417419306335",
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DOI = "doi:10.1016/j.eswa.2019.112915",
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abstract = "Multi-skill resource-constrained project scheduling
problem (MS-RCPSP) is one of the most investigated
problems in operations research and management science.
In this paper, a genetic programming hyper-heuristic
(GP-HH) algorithm is proposed to address the MS-RCPSP.
Firstly, a single task sequence vector is used to
encode solution, and a repair-based decoding scheme is
proposed to generate feasible schedules. Secondly, ten
simple heuristic rules are designed to construct a set
of low-level heuristics. Thirdly, genetic programming
is used as a high-level strategy which can manage the
low-level heuristics on the heuristic domain flexibly.
In addition, the design-of-experiment (DOE) method is
employed to investigate the effect of parameters
setting. Finally, the performance of GP-HH is evaluated
on the intelligent multi-objective project scheduling
environment (iMOPSE) benchmark dataset consisting of 36
instances. Computational comparisons between GP-HH and
the state-of-the-art algorithms indicate the
superiority of the proposed GP-HH in computing feasible
solutions to the problem.",
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notes = "Also known as \cite{LIN2020112915}",
- }
Genetic Programming entries for
Jian Lin
Lei Zhu
Kaizhou Gao
Citations