Colloquia

Summary

This research introduces a simulation framework that optimizes energy logistics for mobile battery systems in construction projects. The model captures dynamic interactions between electric machinery, mobile batteries, and logistics entities. Integrating operational schedules, coupling behaviour, and charging infrastructure. By connecting these interactions through event driven processes, the system dynamically replans resource allocation and charging cycles in response to real time conditions. The framework combines Discrete Event Simulation (DES) and Agent-Based Modeling (ABM) to represent construction operations, while a predictive optimization layer using Decision Tree Regression enhances performance by identifying optimal schedules and initial parameter, such as state of charge, number of batteries, and break schedules to minimize downtime, energy usage and total cost. Results demonstrate that adaptive coordination substantially improves operational efficiency and energy utilization compared to static scheduling. Overall, this work establishes a foundation for intelligent logistical optimization and energy management systems that integrate simulation and machine learning for sustainable, data driven construction operations.