Predicting of the Number of Diners in School Cafeteria; Including COVID-19 Pandemic Period Data

Chae-eun Baek, Yesl Kwon, Jangmin Oh

http://doi.org/10.5626/JOK.2024.51.7.634

Accurately predicting the number of diners in institutional food service is essential for efficient operations, reducing leftovers, and ensuring customer satisfaction. University cafeterias, in particular, face additional challenges in making these predictions due to various environmental factors and changes in class formats caused by the COVID-19 pandemic. To tackle this issue, this study utilized specialized data collected during the pandemic period in university cafeteria environments. The data was used to train and compare the performance of five different models. The three best-performing ensemble tree-based models -- RandomForest, LightGBM, and XGBoost -- were averaged to obtain a final prediction with a Mean Absolute Error (MAE) of 30.96. By regularly providing prediction results to on-campus cafeterias using this final model, practical support can be offered to optimize operations. This study presents an effective methodology for accurately predicting of the number of diners, even in abnormal situations such as the COVID-19 pandemic.