XGBoost with Q-learning for complex data processing in business logistics management |
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| Institution: | 1. College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China;2. School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;3. Business Administration Department, Applied College, Najran University, Najran, Saudi Arabia;4. Shariaa, Educational and Humanities Research Center (SEHRC), Najran University, Najran, Saudi Arabia;5. Department of Industrial & Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia;6. Department of Industrial Engineering, College of Engineering in Al-Qunfudah, Umm Al-Qura University, Makkah 21955, Saudi Arabia;1. Institute of Education Guizhou Normal University, Guizhou, China;2. Guizhou Provincial Educational Governance Modernization Research Center Guiyang 550025, Guizhou, China;3. Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia;4. Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia;5. Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia;6. Business Administration Dept., Applied College, Najran University, Najran, Saudi Arabia;1. Earthquake Research Center, Ferdowsi University of Mashhad, Iran;2. Department of Knowledge and Information Science, Ferdowsi University of Mashhad, Iran |
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| Abstract: | The modern business landscape is characterized by complex technical information, economic globalization, and high customer expectations. These factors have led to significant changes in various industries. To ensure customer satisfaction, companies rely on supply chain management (SCM) for the timely delivery of products and gathering feedback for analysis. The collected customer data is often complex and requires advanced methods for processing and management. To effectively manage demand and supply in real-time, businesses must have the ability to handle complex data. Due to the inefficiency and ineffectiveness of traditional methods for the increased data volume and speed, much-emerging research is being conducted on how to harness complex data in SCM. This paper examines the limitations of conventional methods and introduces an Artificial Intelligence (AI) approach based on Q-Learning algorithm with Extreme Gradient Boosting (QL-XGB) model. The QL-XGB method is applied to select suppliers and predict their future demand for the production of products. It is built on the foundation of accurate data and analysis of supply chain characteristics using metrics such as MAE and RMSE. The results show that the QL-XGB model with an accuracy rate of 96.02% outperforms QL and XGB models with respective accuracy rates of 93.44% and 94.68%. |
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