Abstract
In an Open-Pit Production Scheduling (OPPS) problem, the aim is to determine the mining sequence of an orebody as a block model. In common research methods for scheduling, commodity price and consequently block economic value are considered as fixed values. Due to commodity price time series fluctuations throughout the time, it is necessary to consider the variation of commodity price in each period of the OPPS problem. This paper introduces a new approach for the integration of forecasted copper prices based on stochastic differential equations (SDE) with the conventional integer programming OPPS problem. If the block values are different in each period, the block value is referred to as dynamic, else it is considered static. Since OPPS is known to be an NP-Hard problem and block values vary according to the scheduling period, an efficient Genetic Algorithm (GA) is presented to find feasible solutions. Our proposed algorithm is implemented on Marvin copper orebody. Comparison of the design by the conventional static and the SDE simulation-based dynamic block value methods illustrates that compared to the static design; the total value of the dynamic design is 43% higher than that of static design.
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