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As increasingly diverse tasks are being processed on single multi-functional machine, production scheduling has become a critical issue in the planning and management of manufacturing processes. However, the majority of production scheduling literature ignores machine availability and assumes that machine is available all the time. In reality, machines physically deteriorate with increased usage and time. Thus, there is an intense need for manufacturing industries to reduce unexpected breakdowns and remain competitive, and motivating maintenance operations should be integrated into production scheduling models. With the advancements in sensor and prognostic technologies, machine's condition can be monitored and assessed over time through conducting predictive maintenance. Hence, based on this scheme, this study proposes a single-machinebased scheduling model incorporating production scheduling and predictive maintenance. A machine's effective age and remaining maintenance life are introduced to describe machine degradation. Finally, a numerical example is given; the computational results show that this integrated scheduling model has better performance than those existing models, which proves its efficiency.

The manufacturing industry is undergoing transformation and upgrading from traditional manufacturing to intelligent manufacturing, in which Internet of Things (IoT) technology plays a central role in promoting the development of intelligent manufacturing. In order to solve the problem that low production efficiency and machine utilization lead to serious pollution emissions in the workshop caused by untimely transmission of information in all links of the production and manufacturing process to whole supply chains, this study establishes an intelligent production scheduling and logistics delivery model with IoT technology to promote green and sustainable development of intelligent manufacturing. Firstly, an application framework of IoT technology in production–delivery supply chain systems was established to improve efficiency and achieve the integration of production and delivery. Secondly, an integrated production–delivery model was constructed, which takes into account time and low carbon constraints. Finally, a two-layer optimization algorithm was proposed to solve this integration problem. Through a case study, the results show this integration production–delivery model can reduce the cost of supply chains and improve customer satisfaction. Moreover, it proves that carbon emission cost is a major factor affecting total cost, and it could help enterprises to realize the profit and sustainable development of the environment. The production–delivery model could also support the last kilometer distribution problem and extension under E-commerce applications.

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