Cooperative scheduling for multi-fleet battery swapping in electrified mines: A simulation-based optimization approach

Global carbon reduction policies have accelerated the electrification transition in open-pit mine. To meet the continuous operational demands of mining truck fleets, the battery swapping (BS) mode has emerged as an efficient solution. This study investigates cooperative scheduling of multiple fleets in an open-pit mining system equipped with distributed BS stations and centralized charging facilities, including electric mining trucks and battery delivery vehicles. An innovative discrete event simulation (DES)-based optimization framework is proposed, which leverages the controllability of BS demand and battery supply to coordinate supply- and demand-side operations, thereby unlocking potential efficiencies and deriving an optimal scheduling scheme for mining truck operation, BS activities, and battery logistics. A DES model is developed to simulate the interactions among heterogeneous fleets, various resources and facilities, as well as cascading delays induced by queuing in the operational, BS, and battery pickup processes. Furthermore, the DES model is also employed as a repair tool to rapidly correct infeasible solutions. To address the curse of dimensionality inherent in simulation-based optimization, we propose the non-dominated sorting population-based large neighborhood search (NSPLNS) algorithm, which integrates the advantages of population-based multi-objective search and individual-directed enhancement. A series of customized operators tailored to improving the quality of solutions are designed, and parallel simulations to enhance algorithmic efficiency are explicitly incorporated into the algorithm. A real-world case study from Inner Mongolia, China, is used to evaluate the proposed framework and algorithm. Numerical experiments analyze algorithm performance, the impact of customized operators, and conduct sensitivity analyses. Numerical results demonstrate that the proposed model and algorithm enhance the operational economics by maximizing profit, and improve BS efficiency by minimizing queueing and BS time. The source code of this study is publicly available at: https://github.com/HonghuiZou/NSPLNS .