Real-time task scheduling strategy for 3D printing cloud platforms in health scenes

Abstract

In health scenes, 3D Printing Cloud Platform (3DPCP) needs to cope with unpredictable fluctuations in tasks and resources, but traditional scheduling methods have problems such as incomplete consideration of factors, poor optimization, and weak dynamic adaptability, which make it difficult to meet real-time scheduling requirements. To this end, the real-time task scheduling problem of 3DPCP for health scenes is defined, a real-time task scheduling model is established, the design time of user personalized services is considered, a rescheduling scheme is designed in combination with task variations and device variations, and a scheduling strategy that incorporates dynamic mechanisms and improved multi-objective greywolf optimization algorithms is proposed in order to minimize the integrated scheduling cost and the average delivery time of the product. The findings of simulation experiments show that when equipment changes are not considered, compared with the optimal heuristic algorithm in this field, the average cost of the proposed algorithm is reduced by 2014.1 yuan, and the average delivery time is shortened by 1.52 h. When equipment changes are considered, compared with the multi-objective Genetic Algorithm Dynamic Strategies (GADS), the average cost of the proposed algorithm is reduced by 2984.57 yuan, and the average delivery time is shortened by 0.39 h, which validates the effectiveness of the proposed method.