Optimized Task Scheduling in Cloud Manufacturing with Multi-level Scheduling Model

Cloud Manufacturing (CMfg) utilizes the cloud computing paradigm to provide manufacturing services over the Internet flexibly and cost-effectively, where users only pay for what they use and may access services as needed. The scheduling method directly impacts the overall efficiency of CMfg systems. Manufacturing industries supply services aligned with customer-specific needs recorded in CMfg systems. CMfg managers develop manufacturing strategies based on real-time demand to establish service delivery timing. Many elements influence customer satisfaction, including dependability, timeliness, quality, and pricing. Therefore, CMfg depends on the use of multi-objective and real-time task scheduling. Multi-objective evolutionary algorithms have effectively examined many solutions, such as non-dominant, Pareto-efficient, and Pareto-optimal solutions, using both actual and synthetic workflows. This study introduces a new Multi-level Scheduling Model (MSM) and evaluates its effectiveness by comparing it with other multi-objective algorithms, including the weighted genetic algorithm, the non-dominated genetic sorting Algorithm II, and the starch Pareto evolution algorithm. The primary emphasis is on assessing the efficacy of algorithms and their suitability in commercial multi-cloud setups. The MSM's dynamic nature and adaptive features are emphasized, indicating its ability to effectively handle the complexity and demands of CMfg and resolve the scheduling issue within this environment. Experimental results suggest that MSM outperforms other algorithms by achieving a 20% improvement in makespan.

@article{ZHU2024,
  title     = {Optimized Task Scheduling in Cloud Manufacturing with Multi-level Scheduling Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xiaoli ZHU},
  doi       = {10.14569/IJACSA.2024.0150695},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150695}
}