Pareto Set Learning through Genetic Programming for Multi-Objective Dynamic Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8349

@Article{Xu:TEVC,

• author = "Meng Xu and Yi Mei and Fangfang Zhang and Yew Soon Ong and Mengjie Zhang",
• title = "Pareto Set Learning through Genetic Programming for Multi-Objective Dynamic Scheduling",
• journal = "IEEE Transactions on Evolutionary Computation",
• note = "Early Access",
• keywords = "genetic algorithms, genetic programming, Dynamic scheduling, Job shop scheduling, Real-time systems, Training, Processor scheduling, Decision making, Artificial intelligence, Sequential analysis, Reinforcement learning, Scheduling Heuristics, Automatic Learning, Pareto Set Learning",
• ISSN = "1941-0026",
• DOI = "doi:10.1109/TEVC.2025.3568375",
• abstract = "The multi-objective dynamic flexible job shop scheduling (MO-DFJSS) problem is crucial in modern manufacturing, impacting productivity and operational costs. Genetic Programming (GP) has emerged as a prominent method for MO-DFJSS due to its ability to evolve real-time responsible and effective scheduling heuristics. However, existing GP approaches often learn multiple heuristics for different regions of the Pareto front, making their management and selection complicated in real-world applications. This paper proposes a novel Pareto set learning GP (PSLGP) framework that addresses this limitation by learning a single, preference-conditioned heuristic that encompasses the entire Pareto front based on user preferences. This simplifies scheduling and allows for real-time adaptation to user-defined priorities. The framework employs a novel preference-conditioned heuristic representation that incorporates user preferences as additional inputs, enabling dynamic heuristic adjustments. To efficiently evaluate fitness without increasing training time, a surrogate model is used to estimate individual performance across different preferences, and three new fitness aggregation strategies are designed to ensure effective heuristic alignment across the Pareto front. Experimental results demonstrate that PSLGP significantly outperforms the state-of-the-art multi-objective GP approach, particularly in less busy MO-DFJSS environments, providing a more adaptable and efficient solution for dynamic scheduling challenges. Further analyses of preference influence, solution distribution, and heuristic structure provide evidence that the proposed PSLGP effectively learns preference-conditioned scheduling heuristics that align user preferences with various regions of the Pareto front.",
• notes = "Also known as \cite{10994448}",

}

Genetic Programming entries for Meng Xu Yi Mei Fangfang Zhang Yew-Soon Ong Mengjie Zhang

Citations