  The simultaneous stochastic optimization of mining complexes (SSOMC) is a
large-scale stochastic combinatorial optimization problem that simultaneously
manages the extraction of materials from multiple mines and their processing
using interconnected facilities to generate a set of final products, while
taking into account material supply (geological) uncertainty to manage the
associated risk. Although simulated annealing has been shown to outperform
comparing methods for solving the SSOMC, early performance might dominate
recent performance in that a combination of the heuristics' performance is used
to determine which perturbations to apply. This work proposes a data-driven
framework for heuristic scheduling in a fully self-managed hyper-heuristic to
solve the SSOMC. The proposed learn-to-perturb (L2P) hyper-heuristic is a
multi-neighborhood simulated annealing algorithm. The L2P selects the heuristic
(perturbation) to be applied in a self-adaptive manner using reinforcement
learning to efficiently explore which local search is best suited for a
particular search point. Several state-of-the-art agents have been incorporated
into L2P to better adapt the search and guide it towards better solutions. By
learning from data describing the performance of the heuristics, a
problem-specific ordering of heuristics that collectively finds better
solutions faster is obtained. L2P is tested on several real-world mining
complexes, with an emphasis on efficiency, robustness, and generalization
capacity. Results show a reduction in the number of iterations by 30-50% and in
the computational time by 30-45%.
