A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem
Created by W.Langdon from
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- @Article{ZHU:2021:KBS,
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author = "Lei Zhu and Jian Lin and Yang-Yuan Li and
Zhou-Jing Wang",
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title = "A decomposition-based multi-objective genetic
programming hyper-heuristic approach for the
multi-skill resource constrained project scheduling
problem",
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journal = "Knowledge-Based Systems",
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volume = "225",
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pages = "107099",
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year = "2021",
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ISSN = "0950-7051",
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DOI = "doi:10.1016/j.knosys.2021.107099",
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URL = "https://www.sciencedirect.com/science/article/pii/S0950705121003622",
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keywords = "genetic algorithms, genetic programming,
Decomposition, Multi-objective, Hyper-heuristic,
Resource constrained scheduling",
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abstract = "In this paper, an efficient decomposition-based
multi-objective genetic programming hyper-heuristic
(MOGP-HH/D) approach is proposed for the multi-skill
resource constrained project scheduling problem
(MS-RCPSP) with the objectives of minimizing the
makespan and the total cost simultaneously. First, the
decomposition mechanism is presented to improve the
diversity of solutions. Second, a single-list encoding
scheme and an improved repair-based decoding scheme are
designed to represent individuals and construct
feasible schedules, respectively. Third, ten adaptive
heuristics are developed elaborately to constitute a
list of low-level heuristics (LLHs). Fourth, genetic
programming is employed as the high-level heuristic
(HLH) to generate a promising heuristics sequence from
the LLHs set flexibly. Finally, the Taguchi method of
design-of-experiment (DOE) is conducted to analyze the
performance of parameter settings. The effectiveness of
MOGP-HH/D is evaluated on a typical benchmark dataset
and computational results exhibit the superiority of
the proposed algorithm over the existing methods in
solving multi-objective MS-RCPSP",
- }
Genetic Programming entries for
Lei Zhu
Jian Lin
Yang-Yuan Li
Zhou-Jing Wang
Citations