[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123462-en":3,"doc-seo-123462-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123462,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting the duration of goods transportation delays based on machine learning methods - Research article","This study develops a machine learning-based model to improve the accuracy of transportation delay prediction in supply chains. It addresses parameter tuning and feature selection by combining the Firefly Algorithm with decision tree regression, targeting more reliable delivery time forecasts. The model is trained and tested on the Dataco Smart Supply Chain dataset with 180,519 transaction records, and compared against SVR, MLPRegressor, Lasso Regression, and Bayesian Ridge.","MSCRA 7,3  \n404  \nReceived 19 February 2025 Revised 30 May 2025 Accepted 10 July 2025  \nModern Supply Chain Research and Applications  \nVol. 7 No. 3, 2025  \npp. 404-423  \nEmerald Publishing Limited 2631-3871  \nDOI 10.1108/MSCRA-02-2025-0011  \nPredicting the duration of goods transportation delays based on machine learning methods  \nHossein Mirzaei, Amir Daneshvar and Bijan Nahavandi  \nDepartment of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran  \nAbstract  \nPurpose – 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.  \nDesign/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.  \nFindings – 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.  \nOriginality/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.  \nKeywords Product delay prediction, Decision tree regression, Firefly Algorithm Paper type Research article  \n1. Introduction  \nGlobal supply chains have become essential to modern commerce, facilitating international trade and business operations (Hathikal et al., 2020) . A critical aspect of supply chain management is the timely transportation of goods, as delays can lead to significant financial losses, damaged business relationships, and decreased customer satisfaction (Al-Saghir, 2022) . Transportation delays can arise from various factors, including traffic, weather disruptions, customs issues, or technological limitations (Polimet al., 2017). As the global market grows, minimizing these delays becomes increasingly important.  \nEfficiently managing transportation delays is vital for enhancing operational performance and ensuring customer satisfaction. Predictive models that leverage transport tracking data are key in this context, helping businesses identify delay scenarios and forecast delivery times more accurately. These models provide critical insights, aiding decision-making processes and  \n© Hossein Mirzaei, Amir Daneshvar and Bijan Nahavandi. Published in Modern Supply Chain Research and Applications. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at Link to the terms of th","cbCaiicDTnhBYQPl","https://ap.wps.com/l/cbCaiicDTnhBYQPl","pdf",1478930,1,20,"English","en",105,"# Introduction\n## Problem statement\n## Research question","[{\"question\":\"What is the main purpose of this research?\",\"answer\":\"To improve the accuracy of predicting transportation delays in supply chains by building a machine learning-based model that supports more dependable delivery-time forecasting.\"},{\"question\":\"How does the proposed model work?\",\"answer\":\"It uses decision tree regression enhanced by the Firefly Algorithm, applying the Firefly Algorithm for both parameter optimization and feature selection.\"},{\"question\":\"What dataset and baselines are used for evaluation?\",\"answer\":\"The model is trained and tested on the Dataco Smart Supply Chain dataset with 180,519 transaction records and compared with SVR, MLPRegressor, Lasso Regression, and Bayesian Ridge.\"}]","Predicting the duration of goods transportation delays based on machine learning methods - 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