Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Intege...Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming(MILP) optimization framework. The MILP formulation maximizes the Net Present Value(NPV) of the reserve when extracted with(i) open pit mining,(ii) underground mining, and(iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.展开更多
A parallel version of the traditional grid based cost-to-go function generation algorithm used in robot path planning is introduced. The process takes advantage of the spatial layout of an occupancy grid by concurrent...A parallel version of the traditional grid based cost-to-go function generation algorithm used in robot path planning is introduced. The process takes advantage of the spatial layout of an occupancy grid by concurrently calculating the next wave front of grid cells usually evaluated sequentially in traditional dynamic programming algorithms. The algorithm offers an order of magnitude increase in run time for highly obstacle dense worst-case environments. Efficient path planning of real world agents can greatly increase their accuracy and responsiveness. The process and theoretical analysis are covered before the results of practical testing are discussed.展开更多
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文摘Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming(MILP) optimization framework. The MILP formulation maximizes the Net Present Value(NPV) of the reserve when extracted with(i) open pit mining,(ii) underground mining, and(iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.
文摘A parallel version of the traditional grid based cost-to-go function generation algorithm used in robot path planning is introduced. The process takes advantage of the spatial layout of an occupancy grid by concurrently calculating the next wave front of grid cells usually evaluated sequentially in traditional dynamic programming algorithms. The algorithm offers an order of magnitude increase in run time for highly obstacle dense worst-case environments. Efficient path planning of real world agents can greatly increase their accuracy and responsiveness. The process and theoretical analysis are covered before the results of practical testing are discussed.
