Learning to schedule heuristics for the simultaneous stochastic optimization of mining complexes

• Published in: Computers & operations research 2023-11, Vol.159, p.106349, Article 106349
• Main Authors: Yaakoubi, Yassine, Dimitrakopoulos, Roussos
• Format: Article
• Language: English
• Subjects: Mining complex; Nonlinear stochastic programming; OR in natural resources; Reinforcement learning; Self-learning hyper-heuristic; Simulated annealing
• ISSN: 0305-0548
• DOI: 10.1016/j.cor.2023.106349

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. •The simultaneous stochastic optimization of mining complexes is addressed.•A hyper-heuristic, using an adaptive neighborhood search, is shown.•Reinforcement learning agents guide the search through the solution landscape.•The self-learning hyper-heuristic is tested on real-world mining complexes.•The data-driven heuristic scheduling significantly reduces the execution time.