Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy

Abstract

Diversified customer needs make the production system more susceptible to high-frequency fluctuations of uncertain factors (UFs), which puts forward higher requirements for the real-time and systematic decision-making of the system. The opti-state control strategy enables the system to maintain the adaptive optimal state after being disturbed. The core intelligent synchronisation of the opti-state control strategy needs to perceive the state of the affected system and its degree of change. Aiming at the challenge of difficulty in evaluating the uncertain factors impact degree (UFID) of the complex production logistics system, this work proposes a big data-enabled intelligent synchronisation under the opti-state control strategy. Based on the simulation data of system operation, big data is used to mine the relationship between the UFID and the system states, then use wrapper GA-DNN (Deep Neural Network) feature selection and classification method evaluates the UFID, which will be applied to the synchronisation decision. The results show that the method can accurately evaluate the UFID and avoid the waste of resources and the increase in operating costs caused by excessive evaluation of the UFID, thereby also improves the effectiveness and efficiency of the opti-state control strategy.