Optimal Fleet Management in Open-Pit Mines: A Translation** Optimizing fleet management in open-pit mines is one of the critical challenges for reducing operational costs an‎d improving productivity. In this system, shovels an‎d trucks play a central role, an‎d employing mathematical optimization methods can enhance the precise allocation an‎d scheduling of equipment. The integrated mathematical model for fleet management systems (FMS_IM) is designed to simultaneously address allocation an‎d scheduling issues, providing an efficient approach to improving mining operations. By defining a stage parameter, the model determines fleet status at any moment an‎d eliminates the need for time-related variables in planning. Analyzing three case studies an‎d comparing them with other research revealed that this model significantly improves production an‎d reduces costs. For instance, in the Choghart iron ore mine, implementing this model led to a 12.85% increase in production an‎d a $1,350,000 saving in fleet costs. However, the inherent complexity of large-scale problems an‎d the NP-hard nature of such issues result in challenges like extended solution times an‎d memory constraints. To address these challenges, a two-phase fleet management system model (FMS_TS) was introduced, dividing the problem into allocation (FMS_AS) an‎d scheduling (FMS_SS) phases. Analysis of 400 ran‎dom samples showed that this approach achieved excellent results in a very short time. In eva‎luating FMS_TS, four categories were examined: loading time, material unloading time, queuing time, an‎d truck speeds. The loading category performed the best with 100% consistency, eliminating the need for a second phase execution, while the queuing category showed the lowest consistency at 86%. Nevertheless, the queuing category exhibited the highest improvement percentage with a 12% increase. The solution time for this model varied across categories, an‎d multiple executions of the scheduling phase were required in some cases. However, even in the speed category, which had the highest average solution time, the duration was 34.2 seconds, confirming the methodʹs efficiency. A scenario-based fleet management system (FMS_SBA) was also introduced to address uncertainties such as weather conditions, equipment failures, an‎d queuing times. Results showed that this model performed better in truck allocation an‎d operational cost reduction compared to other methods. Comparing FMS_SBA with another method demonstrated that while the amount of loaded mineral an‎d waste was the same in both models, FMS_SBA utilized larger-capacity trucks an‎d reduced the number of trucks, leading to lower costs. For instance, the operational costs of this model in three scenarios were $18,650,000, $17,800,000, an‎d $17,800,000, respectively, significantly lower than those of the other method. In addition to reducing direct costs, this approach requires fewer personnel for truck management an‎d aligns better with real-world mining conditions. From a time perspective, this model demonstrated remarkable efficiency with an average solution time of 0.95 seconds. These results indicate that using high-capacity trucks in the FMS_SBA model is a more economical an‎d efficient option for managing transportation in open-pit mines.

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حسين خسروشاهي , مهدي ايران پور