Intelligent Queue Optimization and Appointment Scheduling Framework for Public Service Systems using Predictive Analytics
Keywords:
Intelligent, Predictive Analytics, Queue Optimization, Machine Learning, Public Service SystemsAbstract
This study presents an Intelligent Queue Optimisation and Appointment Scheduling Framework for Public Service Systems using Predictive Analytics to address persistent inefficiencies in service delivery such as long waiting times, overcrowding, and poor resource utilisation. The proposed system integrates machine learning–based predictive analytics, intelligent queue optimisation, and adaptive appointment scheduling within a unified architecture designed for public institutions operating in low-resource environments. The framework models customer flow, service personnel, and appointment processes using computational simulation techniques to enable real-time prediction of demand patterns and dynamic allocation of service resources. The key technologies deployed are Python, SimPy, Mesa, Scikit-learn, TensorFlow, MySQL, and a web-based interface for user interaction and system monitoring. The experimental results indicate that the performance of the proposed approach has been significantly improved compared to the standard queuing systems, including reduced waiting time, reduced queue length, enhanced service efficiency, increased customer throughput and higher customer satisfaction. The predictive and adaptive components also improved scheduling accuracy and minimised missed appointments and service conflicts. The system was shown to be acceptable for deployment in low-resource public service settings despite obstacles such as unreliable power supply, inadequate internet connectivity, and limited ICT infrastructure. To recap, the paper recommends the integration of predictive analytics and intelligent optimisation techniques for substantially improving operational efficiency and service delivery in public institutions and presents a scalable approach for the modernisation of public service systems in developing nations.
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