Enterprise Management Resource Scheduling Based on Cloud Computing using Velocity Aided- Grey Wolf Optimization Algorithm

Economic globalization and international competition have led to more production enterprises adopting project management models, which involve customer or order projection. This approach may result in resource waste, labor-intensive project coordination activities, and ongoing adjustments to management procedures. To rationally utilize limited business scheduling resources, research has focused on the application of the velocity-aided- grey wolf optimization algorithm on cloud deep learning platforms. This algorithm is suitable for solving combinatorial optimization challenges and increasing the integration speed and accuracy of the algorithm by increasing the diversity of particle hosts and setting parameters. This study analyses the existing state of multi-project business management, looks at resource allocation, and creates a model for resource allocation across many projects. This method aims to rationally utilize the limited business scheduling resources in the production business. MATLAB simulates a superior configuration scheme, which validates the science and efficacy of the method studied in this research.