Open AccessRGV Dynamic Scheduling Strategy Based on Network Cellular Automaton and Marko Model
Author(s) -
Jun Yin,
N Chen,
Z T Zhang,
S S Chen
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/3/032026
Subject(s) - computer science , mathematical optimization , markov chain , scheduling (production processes) , automaton , stochastic matrix , markov process , dynamic priority scheduling , jump , distributed computing , theoretical computer science , mathematics , machine learning , statistics , schedule , operating system , physics , quantum mechanics
In an intelligent processing system, if a Rail Guide Vehicle (RGV), also known as automated guide vehicle, is provided with the optimal dynamic scheduling strategy, the work efficiency and economic profit of the workshop can be greatly improved. This paper considers the variable number as well as the types of Computer Number Controller (CNC) and regards the real problem as the Job Shop Scheduling Problem (JSSP). To solve JSSP, this paper use Network Cellular Automaton (NCA) to abstract the flexible workshop system and apply the rule of one-step trial-and-error for the core link between the network cellular automaton and the Markov Transition Probability Matrix. Further this system is simulated using Markov chain in probabilities to expand the solution space with the optimal solution among according to the Monte Carlo experimental principle. Finally, the result can jump out of the local optimal solution and approximate the global optimal dynamic scheduling strategy by trial-and-error rule and Transition Probability Matrix. To verify the NCA-Markov model, this paper use an intelligent flexible workshop which produces two-process materials. By simulating this system with the model, the efficiency of the intelligent processing system can be greatly improved.