A mathematical programming viewpoint for solving the ultimate pit problem

Article Abstract:

A network flow algorithm was formulated to determine an ideal design for an open pit mine. The algorithm, which is closely linked with Lerchs and Grossmann's algorithm, utilizes highly efficient data structures based on graphs that illustrate the data and constraints. Unlike Lerchs and Grossmann's algorithm, the network flow algorithm also utilizes information obtained from the combination of graph theoretic methodology and mathematical programming.

Mining & Extractive Industries, Mining, Optimization theory, Mathematical programming, Mine management

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Computational assessment of distributed decomposition methods for stochastic linear programs

Article Abstract:

Adaptations of decomposition methods, namely regularized and linear decompositions, were evaluated in terms of their ability in dealing with stochastic linear programs. For regularized decomposition, its ability in greatly reducing the frequency of iterations in highly complex problems was noted. Such method proved to be highly appropriate in enhancing the convergence of decomposition on complex problems.

Stochastic programming, Decomposition method

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Optimization over the efficient set of a parametric multiple objective linear programming problem

Article Abstract:

Solution algorithms are set out in detail for an optimization problem involving objective linear programming. A numerical example is provided.

Polyhedra, Polyhedrons

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