A new solution to distributed permutation flow shop scheduling problem based on NASH Q-LearningRen, J. F. (Avtor) Ye, C. M. (Avtor) Li, Y. (Avtor) flow shop schedulingdistributed schedulingpermutation flow shopNASH Q-learningmean field (MF)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.Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering20212026-03-03 08:22:30Članek v reviji97384UDK: 331.103:678.02COBISS_ID: 270030595DOI: 10.14743/apem2021.3.399ISSN pri članku: 1854-6250slContent from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.