Simulation of Production Scheduling System in Automated Manufacturing Workshop Based on Markov Model

With the development of science and technology, the fierce market competition and the diversification of customer needs, multi variety and small batch flexible production is becoming the main mode of manufacturing system. The manufacturing contradictions of product quality, production capacity, delivery cycle and production cost are becoming more and more prominent. The traditional production scheduling method can no longer meet the flexible, efficient and automated production goals of modern enterprises. This paper studies the typical NP hard problem of job shop scheduling, and comprehensively analyzes and designs the adaptive system of job shop scheduling from the perspective of system implementation. The results show that from the abstract model, the system is equivalent to 45.26% of a data input. Therefore, analyzing the input-output relationship of job shop scheduling plays an important role in building a practical model. In order to improve the reliability of some key software, many different algorithms have been proposed. Markov chain is an effective method to predict system reliability. In this paper, Markov chain model is proposed to predict the reliability of automatic manufacturing workshop system, and good results are obtained. It has a high impact on engineering practice and theoretical significance for the scheduling algorithm to meet the actual production scheduling problem.