Using Parallel and Distributed Computing Paradigm for Optimization of Ultimate Pit Limit Determination

Efficient parallel computing techniques can make the solution of computationally challenging optimization problems traceable. Optimization problems from varied disciplines can be solved more efficiently through parallel and distributed computing. Optimization of Ultimate Pit Limit (UPL) determination is an important problem of mining engineering. We used a parallel and distributed computing architecture based on Python Remote Objects and Python Optimization Modelling Objects (PyRO-PyOMO), for UPL determination problem. The results show that exploiting parallelism help in achieving 70 % speedup in computation time on various mining datasets. We find that the programming effort associated with efficient parallelization of optimal ultimate pit limit determination using PyRO-PyOMO architecture is highly non-trivial. A similar parallel computing model can be used for the various mathematical models and optimization methods used to solve other optimization problems as well.