Battery-aware integrated scheduling of open-pit electric mining trucks: MIP model and three-stage adaptive large neighbourhood search

The rapid adoption of electric mining trucks in open-pit mining faces a significant scheduling challenge: effectively coordinating transportation tasks with loading/unloading and battery swapping. To address this, we model the integrated operation process as a flow shop with battery swapping, enabling the coordination of these interdependent processes. Given the problem's computational intractability, we develop a three-stage customised ALNS algorithm with proactive battery management, featuring a novel two-dimensional encoding scheme and problem-specific destroy-repair operators. Extensive experiments demonstrate the superior performance of the proposed ALNS over commercial solvers and benchmark metaheuristics. A case study shows the approach can reduce carbon emissions by 31.97% under average grid conditions, translating to an annual reduction of approximately 11.7 thousand tonnes of CO (Formula presented.). This study provides a practical decision-making tool for achieving sustainable and continuous mining production.