%0 Journal Article
%T Production Scheduling of Open Pit Mines Using Particle Swarm Optimization Algorithm
%A Asif Khan
%A Christian Niemann-Delius
%J Advances in Operations Research
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/208502
%X Determining an optimum long term production schedule is an important part of the planning process of any open pit mine; however, the associated optimization problem is demanding and hard to deal with, as it involves large datasets and multiple hard and soft constraints which makes it a large combinatorial optimization problem. In this paper a procedure has been proposed to apply a relatively new and computationally less expensive metaheuristic technique known as particle swarm optimization (PSO) algorithm to this computationally challenging problem of the open pit mines. The performance of different variants of the PSO algorithm has been studied and the results are presented. 1. Introduction Production scheduling of the open pit mines is a difficult and complex optimization problem. It aims to define the most profitable extraction sequence of the mineralized material from the ground that produces maximum possible discounted profit while satisfying a set of physical and operational constraints. Block model representation of the ore body commonly plays the role of the starting point for the planning and production scheduling of the open pit mines. The block model divides the mineralized body and the surrounded rock into a three-dimensional array of regular size blocks. A set of attributes, for example, grade, tonnage, density, and so forth, are then assigned to each one of these blocks estimated using some form spatial interpolation technique, for example, kriging, inverse distance weighting method, and so forth, and the exploratory drill hole sample data. The blocks are then divided into two categories, that is, ore and waste blocks. The blocks whose prospective profit exceeds their processing cost are categorized as an ore block to be sent for processing once mined while the rest are the waste blocks. An economic value is then assigned to each block by taking into account its group; that is, it is categorized as ore or waste, the commodity price, mining, processing, and marketing costs. The next step is to define the final contour of the pit, that is, the limit to which it is economically feasible to mine by solving the ultimate pit limit problem (UPL). The mathematical formulation of the UPL problem can be defined as follows: where represents the economic value of block , represents the total number of blocks in the block model, represents a binary variable corresponding to block which takes the value 1 if block is inside the ultimate pit limits and 0 otherwise, and represents the predecessor group of block . The solution to this problem identifies the
%U http://www.hindawi.com/journals/aor/2014/208502/