Multi-objective evolutionary algorithms for the truck dispatch problem in open-pit mining operations

Abstract

This work is concerned with the efficient allocation of trucks to shovels in operation at open-pit mines. As this problem involves high-value assets, namely mining trucks and shovels, any improvement obtained in terms of operational efficiency can result in considerable financial savings. Thus, this work presents multi-objective strategies for solving the problem of dynamically allocating a heterogeneous fleet of trucks in an open-pit mining operation, aiming at maximizing production and minimizing costs, subject to a set of operational and physical constraints. Two Multi-objective Genetic Algorithms (MOGAs) were specially developed to address this problem: the first uses specialized crossover and mutation operators, while the second employs Path-Relinking as its main variation engine. Four test instances were constructed based on real open-pit mining scenarios, and used to validate the proposed methods. The two MOGAs were compared to each other and against a Greedy Heuristic (GH), suggesting of of the MOGAs as a potential strategy for solving the multi-objective truck dispatch problem for open-pit mining operations.

Evolving deep neural networks for Time Series Forecasting

“…A neuro-evolutive algorithm (NEA) is among the evolutionary algorithms (EAs) used in the design and/or training of an artificial neural network (ANN). Bio-inspired Algorithms (BIOAs) have gained popularity due to their efficiency at solving different non-linear optimization problems [5]. In this context, this paper tests the possibilities of a hybrid system called Artificial Development and Evolution of Deep Neural Networks (ADEANN-Deep) for shor-term energy price prediction using explanatory variables.…”