Akademik Veri Yönetim Sistemi
2nd International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2024, Virtual, Online, 7 - 08 Kasım 2024, cilt.2482 CCIS, ss.245-264, (Tam Metin Bildiri)
Cash in Transit (CIT) involves the transportation of banknotes, coins, and other valuables. The transportation of these items inherently presents certain risks, necessitating security measures to protect the process. This study considers the time-dependent vehicle routing problem, where a fixed-capacity vehicle fleet collects predetermined cash from customers. The study consists of two stages. First, we apply a comparison of the most common machine learning methods, such as multiple linear regression, polynomial regression, decision trees, random forests, support vector machines, multilayer perceptron, and generalized regression neural networks, to predict traffic conditions that directly affect the speed of the vehicles. Second, the estimated speed values are utilized in the mathematical model to minimize travel time between customers during the cash collection operation. Subsequently, a real-life application is conducted in Istanbul to evaluate the effect of the proposed model, and it is solved using GAMS . This study is a starting point for future research focusing on using machine learning to predict dynamic parameters such as traffic density.