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This paper attempts to establish the relationship between quality management system and AI technology, through analyzing the requirements of traditional quality management system of the new era, along with the perception and recognition, analysis and prediction, decision-making ability of AI technology. Based on a lean standardization process of the quality management, involved numbers of the quality tools improved by AI technology, a conceptual framework of integrated quality management information system based on AI are presented, function modules of this framework is discussed on how to implement in detail.

Abstract. In this paper, a hosted on demand cloud based information framework for quality management system is proposed, it offers a customized QMS solution that allow users to access the a robust, secure automated system being enjoyed by hundreds of regulated companies worldwide without having to invest in more expensive infrastructure or employ and train a large internal IT stall. Furthermore, because its quickly deployment over the internet, companies are able to start improving their compliance goals, and reduce time to market immediately.

In most real-world industries, scheduling is processed in a stochastic and dynamic environment, it is necessary to generate a schedule which is suitable for the current system states. Dynamic scheduling solves unimplemented jobs and updates an existing schedule based on the real-time information with minimizing the deviation between the new and original schedules. In this paper, we investigate the dynamic hybrid flow shop scheduling problem and propose a dynamic differential evolution algorithm. In the algorithm, the search space shifts as new jobs arrive, and the problem is solved on a moving horizon based on the real-time information, after the new schedule is established, we update the current schedule. Experiments are carried out to prove the effectiveness of the proposed algorithm.