Open pit mining plans balance production and cost under uncertainty
Chance-Constrained Bi-Objective Evolutionary Optimization for the Open-Pit Mining Operational Planning Problem
Neural and Evolutionary Computing
Summary
Planning mining operations means deciding how to use limited machines and resources while making sure the mined ore meets quality standards. The authors present a method that handles uncertain ore quality by treating ore grades as random variables with known distributions. Their approach tries to produce as much ore as possible while keeping costs low and meeting quality rules using a process called chance constraints. They tested several algorithms to find good trade-offs, showing which ones work best depending on time and quality certainty levels.
What this means in practice
- •For mining operations planners: Create short-term mining schedules that balance ore production and machinery costs while handling uncertain ore quality without heavy simulations.
- •For industrial engineers: Optimize resource allocation in heavy industries facing uncertain input quality by applying similar chance-constrained bi-objective evolutionary methods.
Authors
Ishara Hewa Pathiranage, Aneta Neumann
Abstract
Open-pit mining operational planning involves allocating limited resources while satisfying production, equipment, and ore-quality requirements. Existing approaches often assume deterministic ore grades or rely on simulation-based evaluation under uncertainty. In this paper, we propose a chance-constrained bi-objective formulation of the open-pit mining operational planning problem under uncertain ore grades. We aim to maximize ore production and minimize fleet cost while satisfying stochastic quality requirements through chance constraints. We model ore grades as independent normally distributed random variables and derive a deterministic reformulation of the two-sided chance constraints, avoiding sampling or simulation during fitness evaluation. We evaluate four multi-objective evolutionary algorithms on benchmark instances under different levels and structures of uncertainty. Our results show that NSGA-II and NSGA-III generally obtain the best feasible ore-production values, with NSGA-II requiring less computational time. GSEMO has the lowest computational cost but obtains feasible solutions less consistently and generally provides lower solution quality. We also observe that higher confidence and uncertainty levels reduce feasibility, with the effect depending on the uncertainty structure. The results highlight the importance of considering both the magnitude and structure of uncertainty in short-term open-pit operational planning.