Workload Forecasting of a Logistic Node Using Bayesian Neural Networks

TL;DR

This paper presents a Bayesian Neural Network model for accurately forecasting hourly container truck traffic in depots, aiding workload planning and operational efficiency.

Contribution

It introduces a novel Bayesian deep learning approach for workload forecasting in container depots, validated with real-world data and multiple datasets.

Findings

Model demonstrates high forecasting accuracy on real data

Effective for various data sources with different characteristics

Provides a foundation for workload optimization systems

Abstract

Purpose: Traffic volume in empty container depots has been highly volatile due to external factors. Forecasting the expected container truck traffic along with having a dynamic module to foresee the future workload plays a critical role in improving the work efficiency. This paper studies the relevant literature and designs a forecasting model addressing the aforementioned issues. Methodology: The paper develops a forecasting model to predict hourly work and traffic volume of container trucks in an empty container depot using a Bayesian Neural Network based model. Furthermore, the paper experiments with datasets with different characteristics to assess the model's forecasting range for various data sources. Findings: The real data of an empty container depot is utilized to develop a forecasting model and to later verify the capabilities of the model. The findings show the performance validity of the model and provide the groundwork to build an effective traffic and workload planning system for the empty container depot in question. Originality: This paper proposes a Bayesian deep learning-based forecasting model for traffic and workload of an empty container depot using real-world data. This designed and implemented forecasting model offers a solution with which every actor in the container truck transportation benefits from the optimized workload.