Preference learning based deep reinforcement learning for flexible job shop scheduling problem

Preference learning based deep reinforcement learning for flexible job shop scheduling problem

Abstract The flexible job shop scheduling problem (FJSP) holds significant importance in both theoretical research and practical applications. Given the complexity and diversity of FJSP, improving the generalization and quality of scheduling methods has become a hot topic of interest in both industr...

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Bibliographic Details
Main Authors: Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao
Format: Article
Language:English
Published: Springer 2025-01-01
Series:Complex & Intelligent Systems
Subjects:
Flexible job shop scheduling problem
Preference learning
Proximal policy optimization
Deep reinforcement learning
Online Access:https://doi.org/10.1007/s40747-024-01772-x
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