A fluid relaxation model-integrated dueling double deep Q-network for solving the dynamic multiplicity flexible job-shop scheduling problem
YANG Xiao-yu
HAN Yu-yan
WANG Yu-ting
LI Huan
ZHANG Biao
Abstract：Due to the random arrival of orders during production,companies may face challenges that result in the execution of a scheduling plan that is not optimal.The real-time dynamic arrival of orders is a critical issue.To address the dynamic multiplicity flexible job-shop scheduling problem(DMFJSP),which involves dynamic arrivals of job orders and multiple job types,a multi-policy dueling double deep Q-network(MPD3QN)solution is proposed.Firstly,to reduce the complexity of DMFJSP,a simplified fluid relaxation model is introduced and a multi-criteria selection strategy is developed based on this model to aid production scheduling decisions.Secondly,a Markov decision process(MDP)framework is constructed by extracting 19 state features related to jobs and machines,and 20 composite rules are designed to form the action space.Then,the MPD3QN algorithm is formulated through the integration of prioritised experience replay,a soft update mechanism,and an adaptive action selection strategy.Finally,the proposed method is evaluated through 81 test instances,and its performance is compared against three existing deep reinforcement learning scheduling approaches.Simulation results confirm its superior performance in scheduling efficiency and robustness.
Keywords：flexible manufacturing systemssimplified fluid relaxation modeldeep reinforcement learningmulti-policy dueling double deep Q-networkmultiplicity
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：9( 2332-2340 )