Q-learning assisted multi-objective evolutionary optimization for low-carbon scheduling of open-pit mine trucks

Mine trucks, as the core equipment of discontinuous open-pit mining technology, account for high transportation costs and vast quantities of greenhouse gases. In order to improve transportation efficiency and decrease carbon emissions, rationally scheduling shovel-truck pairs is a necessary issue. Previous studies give less consideration on carbon emissions of trucks that varies with road and driving conditions. To overcome the shortage, a constraint bi-objective optimization model is built for low-carbon scheduling problem of open-pit mine trucks, in which minimizing both idle time and carbon emissions of trucks are taken as the objectives. More especially, the limits on working time, traffic volume and the number of trucks are modeled as the constraints. Carbon emissions is formulated by multistage nonlinear function that takes road condition, load and driving state of trucks into account. As the problem-solver, Q-learning assisted multi-objective evolutionary algorithm is put forward. Four evolution states are defined by analyzing the improvement on feasibility and convergence of the population, and four problem-specific evolution operators are designed to meet different demands of the evolution. Q-learning-based selection strategy is proposed to select the most appropriate operator, with the purpose of improving the evolution efficiency. Experimental results on the real-world instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly.