Preference-Based Multi-Objective Genetic Programming for Energy-Efficient Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8638

@InProceedings{DBLP:conf/cec/Qiao00025,

• author = "Zhuoyin Qiao and Fangfang Zhang and Yi Mei and Mengjie Zhang",
• title = "Preference-Based Multi-Objective Genetic Programming for Energy-Efficient Dynamic Flexible Job Shop Scheduling",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Training, Energy consumption, Job shop scheduling, Processor scheduling, Production, Dynamic scheduling, Energy efficiency, Optimization, Convergence, Energy-Efficient job shop scheduling, Preference-Based multi-objective optimisation",
• isbn13 = "979-8-3315-3432-5",
• timestamp = "Mon, 30 Jun 2025 01:00:00 +0200",
• biburl = "https://dblp.org/rec/conf/cec/Qiao00025.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/CEC65147.2025.11043041",
• DOI = "10.1109/CEC65147.2025.11043041",
• abstract = "Energy-Efficient dynamic flexible job shop scheduling (E-DFJSS) is a valuable real-world combinational optimisation problem. As an important variant of DFJSS, E-DFJSS aims to optimise trade-off between production effectiveness and energy consumption. Genetic Programming (GP) has been successfully used to learn dispatching rules in E-DFJSS. Nevertheless, different users have different preferences on the trade-off between production effectiveness and energy consumption, the studies of preference-based multi-objective optimisation algorithms are limited. These existing related preference-based methods face the issue of premature convergence in the early stage when solving E-DFJSS, which results in insufficient diversity. To address these challenges, we develop a preference-based multi-objective GP approach for E-DFJSS. A fusion r-dominance and achievement scalarising function dominance criterion is embedded into the proposed algorithm to solve E-DFJSS. Experimental results on training and test on three scenarios with four preferences show that the proposed method could improve the effectiveness of learnt scheduling heuristics by balancing diversity and convergence of evolutionary progress.",
• notes = "also known as \cite{qiao:2025:CEC} \cite{11043041}",

}

Genetic Programming entries for Zhuoyin Qiao Fangfang Zhang Yi Mei Mengjie Zhang

Citations