A framework for energy-saving selection and scheduling of equipment resources in a networked manufacturing mode.

The sustainable development and intelligent transformation of the manufacturing industry have become inevitable trends. As a typical example of the intelligent transformation, the networked manufacturing mode has been widely applied through sharing and utilizing manufacturing resources all over the world. Existing research on energy-saving use of manufacturing equipment mainly focuses on internal workshop of enterprises. However, it is also important to consider how to improve the energy efficiency in a networked manufacturing environment. Compared to traditional manufacturing mode, the networked manufacturing mode faces energy-saving challenges such as machining devices are numerous and scattered, manufacturing services are driven by customer orders, and significant random uncertainty occurs in manufacturing processes. This paper proposes a framework to select and schedule equipment resources for energy-saving in a networked manufacturing environment for different stages of manufacturing. First, to increase energy efficiency in the customer orders demand stage, a fuzzy multi-objective decision-making model is developed. This model considers economy, energy consumption, technology, and inter-enterprise collaboration. Second, to balance the competitive interests between customer orders, and to reduce manufacturing energy consumption, costs, and delivery dates in the manufacturing tasks implementation stage, an energy-saving scheduling model for equipment resources based on non-cooperative game theory is established. Then, to deal with random disturbance in a networked manufacturing, a scheduling model based on random chance constraint programming is built to optimize energy, economy, and production efficiency at the active preventive stage. Finally, a cloud platform implementation method of the proposed framework is summarized. The proposed framework helps provide theoretical analysis that is significant to energy-saving allocation of the equipment resources in the networked manufacturing mode. [ABSTRACT FROM AUTHOR]