A Prediction-Enabled Scheduling Framework for Cloud Manufacturing Applications

In the fast-developing fields of smart manufacturing, achieving efficiency is of utmost importance. Cloud Manufacturing (CMfg), in particular, has been widely discussed among various researchers in the recent past. However, it presents the challenge of optimizing cost-effective job assignments across numerous manufacturing providers. To this end, this paper proposes a prediction-enabled scheduling framework (PE-SF) that combines Particle Swarm Optimization-based meta-heuristic algorithm (PSO) and Random Forest (RF) prediction algorithm, represented as PSO-RF, to identify a better manufacturing schedule minimizing the makespan, material cost, and delivery distance of the entire manufacturing workflow. The framework incorporates a cloud-based workflow approach to collaboratively handle the manufacturing requirements of clients across distributed geographical locations. Additionally, the article investigated the performance of PSO-RF with other traditional algorithms such as Genetic Algorithm (GA) and First-In First-Out (FIFO). The evaluation results of scheduling various workload sizes across ten distributed manufacturing units indicate that the proposed PSO-RF algorithm demonstrated a significant efficiency improvement of over 13.3% faster than the other meta-heuristic algorithms of consideration. Additionally, the hybrid algorithmic approach was able to optimize key metrics such as delivery distance and material cost by 18% and 33.8%, respectively.