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


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.

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Divisions: College of Engineering & Physical Sciences
Publication ISSN: 1676-2789
Last Modified: 08 Dec 2023 11:23
Date Deposited: 06 Dec 2019 11:33
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Related URLs: http://abricom. ... vol17-no2-art5/ (Publisher URL)
PURE Output Type: Article
Published Date: 2019-11-30
Accepted Date: 2019-11-01
Authors: Alexandre, Rafael Frederico
Campelo, Felipe (ORCID Profile 0000-0001-8432-4325)
de Vasconcelos, João Antônio



Version: Published Version

Access Restriction: Restricted to Repository staff only

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