Optimizing Online Interval Scheduling Systems with Data-Driven Analytics for Enhanced Resource Utilization

Abstract

Current scheduling mechanisms struggle to accommodate the diverse constraints of real-world time management, leading to inefficiencies for both clients and administrators. This sector of the project addresses the database integration and analytics required for a multi-machine online interval scheduling system. This online interval scheduling system works by prompting the users for a specific time window they want to make the reservation within rather than booking the exact time. The system uses algorithmic approaches to find the most suitable sector of time in a given window to allocate to the user. A key focus is the development of analytics that enable algorithms to assign reservations to the least popular timeslots within users' specified availability windows, thereby optimizing resource distribution and minimizing congestion. Additionally, efficient queries were developed to provide valuable insights to the reservable space owners, such as peak usage times, utilization rates, and patterns of user preferences. This data-driven approach supports more informed decision-making and enhances the overall efficiency and effectiveness of the reservation system. As a result, this project establishes a foundation for more advanced algorithms, suggesting future integration of factors such as expected travel time. Our goal for the future is to refine these algorithms continually, incorporating additional variables to achieve peak scheduling efficiency. By doing so, we aim to develop a more adaptable and effective scheduling system that meets the diverse needs of real-world users.