Thanks Peter, Cecil and Charles! I have attached the code as it stands, sorry for not getting back to you sooner.
It's still messy but it works, that is, it outputs a column of economic block values in IJK order to be input into an open pit optimiser. The code is for an open source pit optimiser, I am currently trying to port C to Go to use the pseudoflow algorithm outlined here (Berkeley.edu). <http://riot.ieor.berkeley.edu/Applications/Pseudoflow/maxflow.html>. If you're interested below is a short introduction: # Introduction The mining industry is a very relevant economic sector. In Chile, copper exports account for about 62.5% of the total exports and represent a 12% of the GDP [1]. Mines can be either open-pit or underground, the actual decision depending on different economic and technical considerations. Open-pit mines are preferred to underground mines because they can reach higher production levels, and have smaller operational costs. However, most of the time, it is necessary to remove material with poor or no ore content (waste) in order to have access to economically profitable material. In order to define what portions of the terrain must be mined at different moments during the life-time of the mine, the planning horizon is discretized into time-periods (or time-slots). In turn, the terrain is divided into regular blocks, which are arranged in a 3-dimensional array (columns 2,3,4). For each block, estimations on the ore content, density and other relevant attributes (...columns) are constructed by using geostatistical methods. A block model, namely, the set of all blocks and their attributes, is the main input to the mine planning process. My csv is a block model. Lerchs and Grossman [2] proposed a very simplified version of the problem in which block destinations are fixed in advance, slope constraints are considered, but capacity or blending constraints are not. In this case, the problem reduces to selecting a subset of blocks such that the contained value is maximized while the precedence constraint induced by the slope angles are held. This problem is known as the ultimate or final pit problem. Lerchs and Grossman presented an efficient(polynomial) algorithm for solving the ultimate pit problem, and showed that reducing the economic value of any given block makes the optimal solution of the ultimate pit problem to shrink, in the sense that, if the values of the blocks decrease, the new solution is a subset of the original one. Therefore, it is possible to produce nested pits and, by trial and error, construct block schedules that satisfy other constraints like capacity of the mill. Present-day commercial software, like Whittle [3][4], is based on these facts. Picard [5] showed that the ultimate pit problem is equivalent to the maximum closure problem in which, given a directed graph G = (V,A) with weight function w defined over the nodes, one looks for a subset of nodes U ⊂ V such that ∑ u ∈ U w(u) is maximal but u ∈ U, (u,v) ∈ A ⇒ v ∈ U. The maximum closure problem, in turn, can be reduced to the min cut problem (for more details see [27]). Using this fact, Hochbaum [6] proposes to attack the ultimate pit problem by means of existing efficient algorithms for the min cut problem. [1] Boland, N., Dumitrescu, I., Froyland, G., and Gleixner, A. (2009). Lp-based disaggregation approaches to solving the open pit mining production scheduling problem with block processing selectivity. Computers & Operations Research , 36(4):1064– 1089. [2] Lerchs, H and Grossmann, I F, (1965). Optimum design of open pit mines, The Canadian Mining and Metallurgical Bulletin, Vol. 58, January, pp.47-54. [3] Alford, C.G. and Whittle, J., (1986). Application of Lerchs– Grossmann pit optimization to the design of open pit mines, In Large Open Pit Mining Conference, AusIMM–IE Aust Newman Combined Group, 1986, 201–207. [4] Osanloo, M., Gholamnejad, J., and Karimi, B. (2008). Long-term open pit mine production planning: a review of models and algorithms. International Journal of Mining, Reclamation and Environment, 22(1):3–35. [5] Picard, J. (1976). Maximal closure of a graph and applications to combinatorial problems. Management Science, 22(11):pp.1268–1272. [6] Hochbaum, D. and Chen, A. (2000). Performance analysis and best implementation of old and new algorithms for the open-pit mining problem. Operations Research, 48:894–914. -- You received this message because you are subscribed to the Google Groups "golang-nuts" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
csv4.go
Description: Binary data
