Optimizing Cost Allocation in Sports Venue Management Using the Frog Leaping Algorithm

Effective cost allocation in sports venue management is crucial for optimizing operational efficiency and maximizing profitability. As sports venues increasingly face the pressure of managing complex resource demands and fluctuating attendance, traditional methods of cost allocation are often insufficient. This research explores the application of the Frog Leaping Algorithm (FLA), a bio-inspired optimization technique, in the context of sports venue management. By simulating the behavior of frogs seeking the most optimal food sources, FLA can dynamically allocate resources across various operational areas such as staffing, security, maintenance, and utilities. This study evaluates the algorithm’s potential to minimize operational costs while maintaining service quality and ensuring profitability. The research also compares FLA’s performance with other optimization techniques, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), focusing on their efficiency, scalability, and adaptability to changing event conditions. Additionally, real-time data collection and predictive analytics are integrated into the optimization process, enhancing decision-making and responsiveness to varying demands. The findings demonstrate that FLA can provide sports venues with a more robust, adaptive, and cost-effective approach to resource allocation, ensuring both sustainability and enhanced operational performance. This paper also discusses the future potential of integrating machine learning and hybrid optimization models to further refine resource allocation strategies in sports venue management. The study concludes with recommendations for venue managers to adopt these advanced techniques to remain competitive and improve profitability in an increasingly dynamic industry.

Optimizing Cost Allocation in Sports Venue Management Using the Frog Leaping Algorithm Keywords:

Cost Allocation , Sports Venue Management , Frog Leaping Algorithm (FLA) , Optimization Techniques , Genetic Algorithms (GA).