Scheduling method of robot flexible flow shops based on Petri nets and supervised learning
LI Jun
LUO Ji-liang
LI Xu-hang
YI Si-jia
NIE Zhuo-yun
Abstract：The scheduling of robot flexible flow shops is a combinatorial optimization problem,which involves searching for an optimal path in the exponentially growing set of event sequences.In order to improve the quality and efficiency of scheduling optimization by taking advantage of the machine learning and heuristic search,a heuristic optimization method is proposed based on the place-timed Petri nets and supervised learning.Firstly,the algorithm is presented to generate a heuristic data set by the executing rules of place-timed Petri nets.Secondly,the fully connected neural network is designed to learn the heuristic function to predict Petri net behavior from data sets.Thirdly,the neural network is used as the heuristic function,and the A*and beam search algorithms are presented for a place-timed Petri net.Finally,taking a robot flexible flow shop as an example,numerical experiments are carried out.The experimental results show that a high-precision heuristic is obtained by the proposed method for the flow shop's place-timed Petri net model,whose average relative error is less than 0.05％.Both A*and beam search algorithms designed by us can be used to quickly solve the optimal or nearly optimal scheduling strategies for the flow shop.
Keywords：place-timed Petri netsfully connected neural networkheuristic functionrobot flexible flow shops
Publication Date：2025-05-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 1008-1016 )