Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{tian:2024:CEC,

• author = "Yuan Tian and Yi Mei and Mengjie Zhang",
• title = "Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Schedules, Sequential analysis, Operations research, Processor scheduling, Buildings, Project management, Project Scheduling, Multiple Modes, Hyper-heuristics",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10612172",
• abstract = "The multi-mode resource-constrained project scheduling problem (MRCPSP) is a challenging problem for researchers and practitioners in operations research and project management. MRCPSP involves both selecting the execution mode for each activity and sequencing the activities in the schedule. Thus, activity prioritisation and mode selection are the two main decisions in building a project schedule. A rule-based heuristic approach is commonly used for solving this problem in practical complex scenarios. However, designing effective project scheduling rules highly relies on the expertise of professionals and domain knowledge. To address the above issue, this paper proposes a genetic programming-based hyper-heuristic (GPHH) to design heuristic rules automatically. Various decision strategies based on decision orders are proposed and their impact on the capacity of GPHH to learn effective scheduling rules is investigated. The experiment results demonstrate the evolved rules generated by GPHH outperform the existing manual heuristic rules and making two decisions simultaneously is identified as the most effective strategy.",
• notes = "also known as \cite{10612172}

  WCCI 2024",

}

Genetic Programming entries for Yuan Tian Yi Mei Mengjie Zhang

Citations