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.
|Journal||Learning and Nonlinear Models|
|Publication status||Published - 30 Nov 2019|
Alexandre, R. F., Campelo, F., & de Vasconcelos, J. A. (2019). Multi-objective evolutionary algorithms for the truck dispatch problem in open-pit mining operations. Learning and Nonlinear Models, 17(2), 53-66. https://doi.org/10.21528/lmln-vol17-no2-art5