An effective deep actor-critic reinforcement learning method for solving the flexible job shop scheduling problem

Lanjun Wan; Xueyan Cui; Haoxin Zhao; Changyun Li; Zhibing Wang

doi:10.1007/s00521-024-09654-6

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An effective deep actor-critic reinforcement learning method for solving the flexible job shop scheduling problem

Lanjun Wan Xueyan Cui Haoxin Zhao Changyun Li Zhibing Wang

Year: 2024 Journal: Neural Computing and Applications Vol: 36 (20)Pages: 11877-11899 Publisher: Springer Science+Business Media

DOI: 10.1007/s00521-024-09654-6

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Keywords:

Reinforcement learning Computer science Job shop scheduling Job shop Scheduling (production processes) Reinforcement Artificial intelligence Industrial engineering Mathematical optimization Flow shop scheduling Engineering Mathematics Psychology Social psychology Schedule

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Scheduling and Optimization Algorithms

Physical Sciences → Engineering → Industrial and Manufacturing Engineering

Elevator Systems and Control

Physical Sciences → Engineering → Control and Systems Engineering

Advanced Control Systems Optimization

Physical Sciences → Engineering → Control and Systems Engineering

An effective deep actor-critic reinforcement learning method for solving the flexible job shop scheduling problem

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