Predictive maintenance has become increasingly prevalent in modern production systems that are challenged by high-mix low-volume production and short production life cycle. It is very helpful to prevent costly equipment failures, and reduce significant production loss caused by unscheduled machine breakdown. Although important, decision models for joint predictive maintenance and production in manufacturing systems have not been fully explored. Therefore, we propose a reinforcement learning based decision model, that brings together production system modeling and approximate dynamic programming. We start from the development of a state-based model by analyzing the dynamics of a multistage production system with predictive maintenance. It provides an approach to quantitatively evaluate the various disruptions as well as the maintenance decision's impact on production. Then a reinforcement learning method is proposed to explore optimal maintenance policies, that optimize the production and maintenance cost. To further improve the performance of the production system, machine stoppage bottlenecks are defined. An event-based indicator is proved to identify bottlenecks with production data. We test the proposed models in simulation case studies. The proposed predictive maintenance decision model is compared with three policies, which are state-based policy (SBP), time-based policy (TBP) and greedy policy (GP). The numerical studies show that the proposed decision model outperforms the policies, and it has the lowest system cost that is 9.68%, 39.07%, and 39.56% lower than SBP, TBP, and GP, respectively. In addition, the research shows that bottleneck identification and mitigation could help manufacturing systems to achieve more than 9.00% throughput improvement.

Grounded in Gidden’s space theory, this case study examines the construction of linguistic identity in Chinese English-as-a-foreign-language (EFL) teachers teaching in a major Chinese city with regard to their language-learning experiences and beliefs about the roles of English as a language within the context of globalization. The data were collected from semi-structured interviews with two Chinese EFL teachers and observations of their classrooms. The narrative and thematic analyses show how two Chinese EFL teachers came to have preferences for moving from the “periphery” to the “center” of a monolingual or multilingual foreign-language community in different ways. The findings not only reveal how English as a language relates to globalization, they also broaden our understanding of the complex formation of identity of the language teachers within a global context.

This study examined the identity development of two Chinese teachers of English as a foreign language (EFL) in a globalized city in China. Grounded in the conceptual frameworks of ‘identity in belief and practice’ and the images of teacher knowledge, the study critically analysed the factors that influenced the formation of teacher identities. The data were collected through interviews and classroom observations. It was revealed that the participants acquired and internalized their professional knowledge differently, resulting in differences in teacher beliefs and instructional practices. Individuals’ acquisition of teaching knowledge did not necessarily lead to practical identities that matched their initial expectations. The gaps between teacher knowledge and practice and between the participants’ ideals and reality acted as parts of their ‘glocal’ identity formation. The findings illuminated the tensions and limitations within the educational transfer between Western-style and non-Western-style classrooms in Chinese teachers’ teaching.