Présentation sur YouTube.

Decision-making in complex and/or stochastic settings, such as industrial mining complexes, presents significant challenges across various sectors. This seminar will delve into a novel solution approach that integrates machine learning and optimization for mineral supply/value chains under supply and demand uncertainties. We will explore three interconnected paradigm shifts: 1) data-driven hyper-heuristics with a heuristic search tree and self-adaptive framework; 2) smart lifelong learning context-aware solvers, with a hyper-heuristic for dynamic mining complex modeling, neural diving policy for heuristic selection, and neural branching policy with a soft branching strategy; and 3) a distributional perspective for warm-starting, using historical solutions and graphical models for initial production schedules. The performance of the proposed approach will be demonstrated through computational results and case studies, showcasing up to three orders of magnitude reductions in primal suboptimality and execution times, and increased robustness in solutions yielding up to 40% higher net present values. Finally, we will discuss insights, potential limitations, and future research directions in developing robust, reasoning, and responsible decision-support systems for industrial-scale uncertain environments.