Open pit mine production schedule optimization using a hybrid of maximum-flow and genetic algorithms |
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| Affiliation: | 1. School of Engineering and Information Technology, University of New South Wales, Canberra, Australia;2. School of Mechanical and Mining Engineering, University of Queensland, Brisbane, Australia;1. Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile;2. Delphos Mine Planning Laboratory, Department of Mining Engineering, Universidad de Chile, Santiago, Chile;3. CSIRO Chile International Centre of Excellence, Santiago, Chile;4. Department of Mathematics, Universidad Técnica Federico Santa María, Av España 1680, Valparaíso 2390123, Chile;5. Technological Institute for Industrial Mathematics (ITMATI), University of Santiago de Compostela, Santiago de Compostela, Spain |
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| Abstract: | Production scheduling is a critical activity for the long-term production planning of open pit mining operations. It deals with the effective management of resources and maximizes cash flows to generate higher profits over the life of a mine. Production scheduling problems determine that blocks be mined and processed over a number of periods subjected to mining and processing constraints, which makes the problem more complex. The complexity is further increased due to the uncertainty in the input parameters. In this study, the maximum flow algorithm with a genetic algorithm is used to generate the long-term production schedule. The graph structure for maximum flow is created for multiple periods under uncertainty, and the flow in the arcs is controlled by a genetic algorithm to develop a production schedule. Numerical results for realistic instances are provided to indicate the efficiency of the solutions. |
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| Keywords: | Combinatorial optimization Production scheduling Maximum flow Genetic algorithm Uncertainty |
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