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<p>The optimization of stope layouts is one of the most critical and computationally demanding challenges in underground mine planning, with direct implications for economic performance, resource efficiency, and environmental sustainability. This review synthesizes the evolution of algorithmic approaches to stope layout optimization—comprising the design of the stope shape and the placement of the stope within the parent orebody, tracing progress from early deterministic heuristics to advanced frameworks based on mathematical programming, network flow formulations, metaheuristics, stochastic optimization, and artificial intelligence. Early fixed-geometry methods provided operational simplicity but were constrained by high dilution, ore loss, and limited adaptability to geological variability. Subsequent developments in variable-size and hybrid optimization algorithms improved geometric conformity to orebodies, leading to enhanced ore recovery and reduced waste extraction. More recent research integrates stope design with production scheduling, explicitly represents geological uncertainty, and incorporates data-driven and digital-twin technologies to support continuous decision making in uncertain conditions. While much of the literature has focused on maximizing economic value under technical constraints, this review highlights the growing convergence between optimization performance and resource-efficiency and circular mining principles supporting sustainable production. Algorithms that effectively control dilution, improve material selectivity, and enhance robustness under uncertainty are shown to simultaneously reduce energy intensity, waste, and tailings generation, thereby improving mineral resource retention and reducing irreversible material losses. The findings emphasize the need for scalable, multi-objective, and uncertainty-aware optimization frameworks that integrate geotechnical stability, economic value, and environmental performance to support resilient, resource-efficient, and circular underground mining operations aligned with sustainable production objectives.</p>

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A Review of Stope Layout Optimization Algorithms: Linking Resource Efficiency, Circular Mining, and Sustainable Production

  • 期刊论文

  • 发表日期：

    2026年7月4日

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  • Solomon Opoku Acheampong,
  • Ezzeddin Bakhtavar,
  • Eugene Ben-Awuah

摘要

The optimization of stope layouts is one of the most critical and computationally demanding challenges in underground mine planning, with direct implications for economic performance, resource efficiency, and environmental sustainability. This review synthesizes the evolution of algorithmic approaches to stope layout optimization—comprising the design of the stope shape and the placement of the stope within the parent orebody, tracing progress from early deterministic heuristics to advanced frameworks based on mathematical programming, network flow formulations, metaheuristics, stochastic optimization, and artificial intelligence. Early fixed-geometry methods provided operational simplicity but were constrained by high dilution, ore loss, and limited adaptability to geological variability. Subsequent developments in variable-size and hybrid optimization algorithms improved geometric conformity to orebodies, leading to enhanced ore recovery and reduced waste extraction. More recent research integrates stope design with production scheduling, explicitly represents geological uncertainty, and incorporates data-driven and digital-twin technologies to support continuous decision making in uncertain conditions. While much of the literature has focused on maximizing economic value under technical constraints, this review highlights the growing convergence between optimization performance and resource-efficiency and circular mining principles supporting sustainable production. Algorithms that effectively control dilution, improve material selectivity, and enhance robustness under uncertainty are shown to simultaneously reduce energy intensity, waste, and tailings generation, thereby improving mineral resource retention and reducing irreversible material losses. The findings emphasize the need for scalable, multi-objective, and uncertainty-aware optimization frameworks that integrate geotechnical stability, economic value, and environmental performance to support resilient, resource-efficient, and circular underground mining operations aligned with sustainable production objectives.