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<p>Truck–shovel haulage is the dominant transport mode in open-pit mining, but fleet decisions are complicated by stochastic cycle times, downtime, and time-varying haul-road conditions. This paper presents a tightly coupled discrete-event simulation (DES)–optimization framework for the Zarshuran open-pit gold mine to co-optimize truck fleet size/composition and dispatch–routing control parameters under a dynamic cost model. The DES is calibrated from a seven-day time study; equipment reliability is represented by a two-state continuous-time Markov chain, and haul-road deterioration updates travel times with accumulated traffic. Three transition scenarios are solved with a hybrid HWOA–NSGA-II algorithm; each candidate design is evaluated using 30 DES replications, and the Pareto set is post-processed using TOPSIS under a minimum production requirement. Quantitatively, the baseline replacement-path design produces 16,080 t/shift at 1,513.3 USD/a (~ 1.13 USD/t). With the production constraint, the top-ranked mixed-fleet solution achieves 28,817 t/shift at 2,852.2 USD/h (~ 1.19 USD/t), increasing throughput by 79.2% while keeping the estimated unit-cost change within ~ 5.2% relative to the baseline.</p>

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Hybrid DES and HWOA–NSGA-II for Pareto-optimal Open-pit Truck-and-Shovel Fleet Design

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Amir Batarbiat,
  • Mojtaba Rezakhah

摘要

Truck–shovel haulage is the dominant transport mode in open-pit mining, but fleet decisions are complicated by stochastic cycle times, downtime, and time-varying haul-road conditions. This paper presents a tightly coupled discrete-event simulation (DES)–optimization framework for the Zarshuran open-pit gold mine to co-optimize truck fleet size/composition and dispatch–routing control parameters under a dynamic cost model. The DES is calibrated from a seven-day time study; equipment reliability is represented by a two-state continuous-time Markov chain, and haul-road deterioration updates travel times with accumulated traffic. Three transition scenarios are solved with a hybrid HWOA–NSGA-II algorithm; each candidate design is evaluated using 30 DES replications, and the Pareto set is post-processed using TOPSIS under a minimum production requirement. Quantitatively, the baseline replacement-path design produces 16,080 t/shift at 1,513.3 USD/a (~ 1.13 USD/t). With the production constraint, the top-ranked mixed-fleet solution achieves 28,817 t/shift at 2,852.2 USD/h (~ 1.19 USD/t), increasing throughput by 79.2% while keeping the estimated unit-cost change within ~ 5.2% relative to the baseline.