A Hybrid Genetic Algorithm-Random Forest Regression Method for Optimum Driver Selection in Online Food Delivery

Abstract

The online food delivery trend has become rapid due to the COVID-19 incident, which limited mobility, while the broader challenge in the online food delivery system is maximizing quality of service (QoS). However, studies show that driver selection and delivery time are important in customer satisfaction. The solution is our research aim, which is the selection of optimal drivers for online food delivery using random forest regression and the genetic algorithm (GA) method. Our research contribution is a novel approach to minimizing delivery time in online food delivery by combining a random forest regression model and genetic algorithms. We compare random forest regression with three other state-of-the-art regression models: linear regression, k-nearest neighbor (KNN), and adaptive boosting (AdaBoost) regression. We compare the four models with metrics including , mean squared error (MSE), root mean squared error (RMSE), mean total error (MAE), and mean absolute percentage error (MAPE). We use the optimum model as the fitness function in GA. The test results show that random forest performs better than linear, KNN, and AdaBoost regression, with an , RMSE, and MAE value of 0.98, 54.3, and 11, respectively. We leverage the optimum random forest regression model as the GA fitness function. The best efficiency is reducing the delivery time from 54 to 15 minutes, achieved through rigorous testing on various cases. In addition, by completing this research, we also achieve some practical implications, such as an increase in customer satisfaction, a reduction in cost, and a paramount finding in the field of data-driven decision-making. The first key finding is an optimum driver selection model in random forest regression, while the second is an optimum driver selection model in GA.