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The simultaneous stochastic optimization of mining complexes (SSOMC) is alarge-scale stochastic combinatorial optimization problem that simultaneouslymanages the extraction of materials from multiple mines and their processingusing interconnected facilities to generate a set of final products, whiletaking into account material supply (geological) uncertainty to manage theassociated risk. Although simulated annealing has been shown to outperformcomparing methods for solving the SSOMC, early performance might dominaterecent performance in that a combination of the heuristics' performance is usedto determine which perturbations to apply. This work proposes a data-drivenframework for heuristic scheduling in a fully self-managed hyper-heuristic tosolve the SSOMC. The proposed learn-to-perturb (L2P) hyper-heuristic is amulti-neighborhood simulated annealing algorithm. The L2P selects the heuristic(perturbation) to be applied in a self-adaptive manner using reinforcementlearning to efficiently explore which local search is best suited for aparticular search point. Several state-of-the-art agents have been incorporatedinto L2P to better adapt the search and guide it towards better solutions. Bylearning from data describing the performance of the heuristics, aproblem-specific ordering of heuristics that collectively finds bettersolutions faster is obtained. L2P is tested on several real-world miningcomplexes, with an emphasis on efficiency, robustness, and generalizationcapacity. Results show a reduction in the number of iterations by 30-50% and inthe computational time by 30-45%.

Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes