Archives > Volume 16 | Number 3 | September 2021 > pp 269–284
Advances in Production Engineering & Management
Volume 16 | Number 3 | September 2021 | pp 269–284
A new solution to distributed permutation flow shop scheduling problem based on NASH Q-Learning
Ren, J.F.; Ye, C.M.; Li, Y.
ABSTRACT AND REFERENCES (PDF) |
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A B S T R A C T
Aiming at Distributed Permutation Flow-shop Scheduling Problems (DPFSPs), this study took the minimization of the maximum completion time of the workpieces to be processed in all production tasks as the goal, and took the multi-agent Reinforcement Learning (RL) method as the main frame of the solution model, then, combining with the NASH equilibrium theory and the RL method, it proposed a NASH Q-Learning algorithm for Distributed Flow-shop Scheduling Problem (DFSP) based on Mean Field (MF). In the RL part, this study designed a two-layer online learning mode in which the sample collection and the training improvement proceed alternately, the outer layer collects samples, when the collected samples meet the requirement of batch size, it enters to the inner layer loop, which uses the Q-learning model-free batch processing mode to proceed, and adopts neural network to approximate the value function to adapt to large-scale problems. By comparing the Average Relative Percentage Deviation (ARPD) index of the benchmark test questions, the calculation results of the proposed algorithm outperformed other similar algorithms, which proved the feasibility and efficiency of the proposed algorithm.
A R T I C L E I N F O
Keywords • Flow shop scheduling; Distributed scheduling; Permutation flow shop; Reinforcement learning; NASH Q-learning; Mean field (MF)
Corresponding author • Ye, C.M.
Article history • Received 29 July 2021, Revised 8 September 2021, Accepted 26 September 2021
Published on-line • 31 October 2021
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