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Enhancing Input Parameter Estimation by Machine Learning for the Simulation of Large-Scale Logistics Networks

The quality of large-scale logistics network simulation highly depends on the estimation of its key input parameters, which are usually influenced by various factors that are difficult to obtain. To tackle this challenge, this paper proposes a framework to estimate these parameters with high precision through machine learning, in which the impacting factors are divided into static and dynamic groups and used as features to train a learning model for estimation. To overcome the obstacle that dynamic factors are hard to obtain in some scenarios, the proposed framework employs unsupervised learning to analyze their patterns and extract time-invariant features for modeling. A validation study is conducted on the estimation of distribution center sorting times. The results proved our approach can generate more accurate estimation of input parameters, even with the shift of operational plans and absence of relevant data.