Predicting the duration of goods transportation delays based on machine learning methods

Purpose This study aims to improve the accuracy of transportation delay prediction in supply chains by developing a machine learning-based model. The proposed approach addresses the challenges of parameter tuning and feature selection by integrating the Firefly Algorithm with decision tree regression, helping businesses mitigate the negative impacts of delivery uncertainties. Design/methodology/approach The study employs a hybrid machine learning approach using decision tree regression enhanced by the Firefly Algorithm for both parameter optimization and feature selection. The model is trained and tested on the publicly available Dataco Smart Supply Chain dataset, consisting of 180,519 transaction records. The performance of the proposed method is compared with four baseline regression techniques: SVR, MLPRegressor, Lasso Regression and Bayesian Ridge. Findings The proposed method significantly outperformed the baseline models across all evaluation metrics. It achieved an R2 score of 0.987, the highest among the tested models and reported the lowest errors in MAE, MSE and MSLE. The Firefly Algorithm effectively enhanced prediction performance by selecting relevant features and tuning model parameters, leading to improved generalizability and reduced overfitting. Originality/value This research introduces a novel integration of the Firefly Algorithm with decision tree regression for delay prediction in logistics, demonstrating superior accuracy and computational efficiency. The approach offers practical value for real-world logistics environments by enabling more reliable delivery time forecasts without the need for high-performance computing infrastructure. It also fills a critical gap in the literature by showing the benefits of combining feature selection and hyperparameter optimization in a single workflow.