Kavli Affiliate: Xian Chen
| First 5 Authors: Xian Chen, Rong Qu, Jing Dong, Ruibin Bai, Yaochu Jin
| Summary:
Dynamic scheduling in real-world environments often struggles to adapt to
unforeseen disruptions, making traditional static scheduling methods and
human-designed heuristics inadequate. This paper introduces an innovative
approach that combines Genetic Programming (GP) with a Transformer trained
through Reinforcement Learning (GPRT), specifically designed to tackle the
complexities of dynamic scheduling scenarios. GPRT leverages the Transformer to
refine heuristics generated by GP while also seeding and guiding the evolution
of GP. This dual functionality enhances the adaptability and effectiveness of
the scheduling heuristics, enabling them to better respond to the dynamic
nature of real-world tasks. The efficacy of this integrated approach is
demonstrated through a practical application in container terminal truck
scheduling, where the GPRT method outperforms traditional GP, standalone
Transformer methods, and other state-of-the-art competitors. The key
contribution of this research is the development of the GPRT method, which
showcases a novel combination of GP and Reinforcement Learning (RL) to produce
robust and efficient scheduling solutions. Importantly, GPRT is not limited to
container port truck scheduling; it offers a versatile framework applicable to
various dynamic scheduling challenges. Its practicality, coupled with its
interpretability and ease of modification, makes it a valuable tool for diverse
real-world scenarios.
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