A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Lin:2020:ESA,

• author = "Jian Lin and Lei Zhu and Kaizhou Gao",
• title = "A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem",
• journal = "Expert Systems with Applications",
• year = "2020",
• volume = "140",
• pages = "112915",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Hyper-heuristic, Multi-skill, Project scheduling",
• ISSN = "0957-4174",
• URL = "http://www.sciencedirect.com/science/article/pii/S0957417419306335",
• DOI = "doi:10.1016/j.eswa.2019.112915",
• 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.",
• notes = "Also known as \cite{LIN2020112915}",

}

Genetic Programming entries for Jian Lin Lei Zhu Kaizhou Gao

Citations