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

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@Article{ZHU:2021:KBS,

• author = "Lei Zhu and Jian Lin and Yang-Yuan Li and Zhou-Jing Wang",
• title = "A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem",
• journal = "Knowledge-Based Systems",
• volume = "225",
• pages = "107099",
• year = "2021",
• ISSN = "0950-7051",
• DOI = "doi:10.1016/j.knosys.2021.107099",
• URL = "https://www.sciencedirect.com/science/article/pii/S0950705121003622",
• keywords = "genetic algorithms, genetic programming, Decomposition, Multi-objective, Hyper-heuristic, Resource constrained scheduling",
• 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