Digital twin-enabled real-time heavy equipment scheduling with rolling-horizon optimization

Abstract

Purpose Unstructured and variable construction sites pose significant challenges to earthwork scheduling. However, current parameter-tuning strategies fall short in supporting dynamic optimization under such complex conditions, and real-time construction data remains underutilized. To address these gaps, this study aims to propose a method that enables the automatic and real-time allocation and scheduling of heavy construction equipment by fully leveraging real-time construction data and dynamic equipment status information. Design/methodology/approach This research proposes a digital twin (DT)-enabled real-time rolling-horizon optimization (RHO) method that integrates an online Improved Aquila Optimizer with an adaptive parameter adjustment strategy. This method uses real-time resource status and construction progress data obtained from high-fidelity DT to dynamically optimize equipment scheduling without manual intervention. Findings The proposed method was implemented for a large hydropower construction project in Southwest China. The experimental results showed that this method can achieve the dynamic allocation and scheduling of heavy construction equipment with high quality and superior performance, significantly reducing construction delays by 38.4%. Originality/value This study presents a DT-enabled real-time RHO method for the automatic and real-time allocation and scheduling of heavy construction equipment. It provides an effective solution to the challenges posed by unstructured and dynamic construction environments, significantly enhancing operational efficiency and reducing scheduling delays.