ADAPTIVE OPTIMIZATION OF THE MINING SCHEDULE OF THE MINING CLUSTER USING A HEURISTIC MULTI-CRITERION ALGORITHM

Abstract

The study provides the scientific substantiation and formalization of an approach to determining the mining schedule at the scale of a mining cluster that integrates several open pits and technogenic deposits. The starting premise is the need to stabilize mill feed – both tonnage and head grade – under spatial grade variability, a dynamic cut-off grade, and changing external drivers. The literature analysis confirms an evolution from deterministic pit-limit and calendar scheduling approaches to formulations that account for geological uncertainty and the requirements of blend stabilization. These findings motivate the choice of evolutionary methods as the basic computational paradigm for obtaining robust solutions in a discrete, multi-objective, and tightly constrained search space. A mathematical formulation is proposed in which the objective function minimizes total mining, haulage, and stockpiling costs while simultaneously bounding deviations from target tonnage and head grade. The constraint set enforces tonnage- and grade-balance, mine and plant throughput, mining sequence, capacities and operating modes of technogenic deposits/stockpiles, as well as blending conditions across streams from different pits. Methodologically, the work employs analysis and synthesis of the technical literature and the construction of a multiperiod model, followed by the justification of an evolutionary search procedure – specifically, a genetic algorithm – as suitable for a discrete, tightly constrained, and multi-criteria mine-scheduling setting. The article presents the problem statement, variable structure, objective function, and constraints. The practical effect lies in improving control of ore-stream quality and reducing costs through coordinated balancing of extraction, stockpiling, and reclaim from technogenic deposits, providing a basis for adaptive mine scheduling under dynamic conditions.