SIM Card Delivery Time Prediction Based on the Interpretable NSGA‐III‐XGBoost

To mitigate the risks posed by the forgery of SIM card information, couriers must conduct face‐to‐face real‐name verification with consumers during door‐to‐door delivery. However, due to various factors, the failure rate of the first delivery handover is high, leading to the need for one or even multiple appointment deliveries. Moreover, the appointment delivery time is often broad, making it difficult to provide precise guidance for couriers' delivery plans, thereby affecting overall delivery efficiency. To address the above issues, this paper explores the problem of predicting the delivery time of SIM cards in the case of first delivery failure. First, this paper comprehensively considers factors that affect the delivery time, including distance, weather, day of the week, and consumer features. These factors are incorporated into the prediction model to make the model more applicable to real‐world scenarios. Second, this paper constructs the NSGA‐III‐XGBoost model. This model uses XGBoost (eXtreme Gradient Boosting) as the base model and optimizes the hyperparameters of XGBoost by using NSGA‐III (Non‐dominated Sorting Genetic Algorithm III). Experimental results show that the proposed model outperforms several benchmark models in terms of prediction accuracy and stability. Finally, this paper uses the SHAP (Shapley Additive exPlanations) method to explain the prediction results of the NSGA‐III‐XGBoost model and deeply explores the impact of various features on delivery time prediction. Based on the experimental results, this paper summarizes the research findings and provides references for model construction and experimental design. By predicting the delivery time of SIM cards in the case of first delivery failure, this paper provides couriers with a reference for the final delivery time, helping them make reasonable arrangements within the time slots scheduled with consumers and thereby enhancing delivery efficiency.