Multi-Objective Simulation-Based Optimization of Truck-Shovel Systems in Open-Pit Mining Using a Hybrid Algorithm

  • سال انتشار: 1403
  • محل انتشار: سومین کنفرانس بین المللی مهندسی مواد، صنایع و پزشکی با رویکرد هوش مصنوعی
  • کد COI اختصاصی: YTCONF03_012
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 11
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نویسندگان

Tahereh Khajvandsany

Department of Industrial Engineering, No.C., Islamic Azad University, Noor, Iran

Hossein Amoozad Khalili

Department of Industrial Engineering, Sar.C., Islamic Azad University, Sari, Iran

Ramezan Rezaeyan

Department of statistic and mathematics, No.C., Islamic Azad University, Noor, Iran

Kourosh Nemati

Department of Computer Engineering, No.C., Islamic Azad University, Noor, Iran

چکیده

Truck-shovel systems are among the most crucial components in open-pit mining operations, directly influencing operational efficiency, equipment utilization, and transportation costs. Given the multi-objective nature of such systems-balancing production, cost, and resource usage traditional optimization methods often fall short in capturing their dynamic and uncertain behavior. This paper presents a hybrid simulation-based optimization approach that integrates Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Differential Evolution (DE), and Variable Neighborhood Search (VNS). The proposed method is designed to improve the balance between global exploration and local exploitation in solving complex multi-objective problems. A discrete-event simulation model of the truck-shovel system at the Sarcheshmeh copper mine was developed using Arena software to evaluate performance under real-world constraints and stochastic conditions. Experimental results show that the hybrid algorithm outperforms individual metaheuristics in terms of both cost and throughput. Specifically, it achieved a ۱% reduction in monthly transportation costs and a ۱۰% increase in average production throughput. These findings highlight the effectiveness of the proposed hybrid method as a practical decision-support tool for mining operations, enabling more efficient equipment planning and resource allocation under uncertainty.

کلیدواژه ها

Simulation-Based Optimization, Truck-Shovel System, Hybrid Metaheuristic Algorithm

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