Learning to schedule heuristics for the simultaneous stochastic optimization of mining complexes
针对采矿综合体同时随机优化这一大规模组合优化问题，提出了一种数据驱动的超启发式框架，通过强化学习自适应选择扰动策略，相比现有方法可将执行时间减少80%，并显著降低次优性。
The simultaneous stochastic optimization of mining complexes is a large-scale stochastic combinatorial optimization problem that simultaneously manages the extraction of materials from multiple mines and their processing through interconnected facilities to generate a set of final products, while taking into account geological (material supply) uncertainty to manage the associated risk. Existing methods do not offer solutions to such a complex problem in a reasonable time. This work proposes a data-driven framework for heuristic scheduling in a fully self-managed hyper-heuristic to solve the simultaneous stochastic optimization of mining complexes. The proposed learn-to-perturb hyper-heuristic is a multi-neighborhood simulated annealing algorithm that selects the heuristic (perturbation) to be applied in a self-adaptive manner using reinforcement learning to efficiently explore the local search that is best suited to a particular search point. By learning from data that describes the performance of the heuristics, a problem-specific ordering of heuristics that collectively find better solutions faster is obtained. To the best of our knowledge, this research proposes the first data-driven heuristic search tree for mine planning. Results from several instances of two types of large-scale industrial mining complexes show a reduction of up to 80% in execution time and an order of magnitude reduction in primal suboptimality compared to state-of-the-art methods.