Duc Duy Nguyen

This study addresses a routing problem at a last-mile company in Vietnam. It proposes using a machine learning technique-Nearest Neighbor Search-to identify the most similar known solution, which is then used as the initial solution for a branch-and-bound algorithm. Firstly, the machine learning investigated a dataset of 500 pre-solved problems to find solutions for new problems. Then, the solution found is fed to the solver as a hint. The experimental results indicate that the algorithm can retrieve solutions for similar cases in just a few milliseconds. For entirely new problems, the method reduces computation time compared to the traditional branch-and-bound algorithm. This significant improvement in efficiency demonstrates the method’s practical value in real-world logistics operations. The combination of machine learning and classical optimization in this approach shows promise for enhancing decision-making processes in the logistics industry, particularly in scenarios requiring rapid and reliable route planning.

Overall Equipment Effectiveness (OEE) stands as a key performance metric widely adopted in the manufacturing industry, aiding in enhancing productivity. This metric offers a comprehensive overview to higher management, enabling them to identify equipment-related losses. With the advancements in Industry 4.0 technologies, the Manufacturing Execution System facilitates real-time data collection, enhancing production efficiency and reducing manufacturing costs. However, the real-time depiction of OEE often fails to provide decision-makers with timely insights to conduct their tasks effectively. This study formulated machine learning models to tackle this challenge by forecasting the OEE for the upcoming working shift. Firstly, the historical dataset encompasses 31 features collected and processed to estimate the OEE value. Then, prominent machine learning models were utilized as prediction models: Linear Regression, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. The results show that the Extreme Gradient Boosting performs well for the OEE prediction with accuracy in training higher than 99% and testing nearly 90%. Our study illustrates an actionable knowledge-discovery process using a real-world data mining approach for the manufacturing industry, potentially applicable to other sectors.