A Mixed-Integer Linear Programming Approach to Optimize Tennis Regimens for Young Athletes in Korea
DOI:
https://doi.org/10.47611/jsrhs.v13i1.6393Keywords:
Mixed-Integer Linear Programming (MILP), Objective Function, Optimization, Prospective Athletes, South Korea, Tennis Training, Training Regimens, Optimization, JuliaAbstract
The popularity of tennis has been surging among young individuals in South Korea, as it offers an appealing alternative to other sports like golf. With this surge in popularity, there is a corresponding increase in competitive intensity, making the optimization of training regimens crucial for success. While athletes in this sport commonly engage in a variety of training activities there has been limited systematic research that offers a quantitatively validated approach for balancing these activities to maximize on-court performance. This study aims to fill this gap by providing an optimization model tailored to the specific requirements of tennis training. Through surveying 48 prospective athletes in South Korea, the study establishes upper and lower bounds for each of 4 activities: Serving Practice, Ground Strokes Practice, Physical Conditioning, and Mental Training. Multiple regression analysis is then used to calculate the weights of each training activity in an objective function, formulated to measure on-court performance. A Mixed-Integer Linear Programming (MILP) model is employed to allocate optimal weekly training hours to each activity, based on these weights and constraints. The study concludes by identifying an optimal training schedule that maximizes the value of the objective function, thereby optimizing on-court performance. The convergence of the objective and best bound at a low level further substantiates the optimality of the discovered solution.
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