University Organizational Workflow Optimization based on Particle Swarm Optimization Algorithm

Optimization of university organizational workflows is an urgent challenge with mounting administrative complexities, dynamic resource allocation, and changing institutional requirements. The conventional workflow management systems depend on rule-based approaches and manual interventions, and as a result, tend to contribute to inefficiencies, delays, and second-best decision-making. The current optimization methods, such as heuristic and statistical models, fail to accommodate real-time changes and are incapable of representing sophisticated dependencies in the administrative processes. To overcome these drawbacks, this work investigates an intelligent optimization model that improves workflow scheduling, resource allocation, and task handling. Based on advanced computation methods, the suggested solution vastly enhances operation effectiveness, decision accuracy, and flexibility to dynamic university settings. Experimental evaluations on real-world university workflow datasets demonstrate a significant improvement in efficiency, achieving a 95% optimization accuracy compared to conventional workflow management techniques. Test evaluations using actual workflow data illustrate a considerable process efficiency boost with higher accuracy and optimization rate than traditional workflow models. This research offers an intelligent and scalable solution for workflow management in universities, helping to improve institutional operations and resource usage.