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@PHDTHESIS{Khan:538444,
author = {Khan, Asif},
othercontributors = {Niemann-Delius, Christian and Lübbecke, Marco},
title = {{D}evelopment of new metaheuristic tools for long term
production scheduling of open pit mines},
school = {Aachen, Techn. Hochsch.},
type = {Dissertation},
address = {Aachen},
publisher = {Publikationsserver der RWTH Aachen University},
reportid = {RWTH-2015-05306},
pages = {XII, 102 S. : Ill., graph. Darst.},
year = {2016},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2016; Aachen, Techn. Hochsch., Diss., 2015},
abstract = {Long term production scheduling of open pit mines is a
large scale and complex optimization problem that has been
extensively discussed in the technical literature since
1960s. It seeks to specify such an extraction sequence of
ore and waste materials from the ground that maximizes the
Net Present Value (NPV) of the operation while satisfying a
set of physical and operational constraints. Block model
representation of the orebody is commonly used as a basic
input for this purpose. The block model discretize the ore
deposit into a three dimensional array of regular sized
blocks. A real sized open pit mine may contain thousands to
millions of blocks of these blocks that may be needed to be
scheduled over a time horizon typically ranging from 5 to 30
years which makes it a large combinatorial optimization
problem. This thesis presents a framework that aims to
handle the above mentioned computationally expensive problem
of the open pit mines with low to moderate computational
cost. To handle the scheduling problem more efficiently the
proposed framework converts it into optimum depth
determination problem. This so called optimum depth
determination problem aims to find the optimum depth to be
mined along a particular column of the block model in a
certain period. In this way this framework helps to avoid
computationally expensive scheduling decisions making
process on the block level. The framework then uses a real
valued / continuous population based metaheuristic technique
to search the solution space for finding optimum or near to
optimum solution of this so called optimum depth
determination problem and consequently of the production
scheduling problem. Different framework specific operators
such as solution encoding, back transform, slope
normalization etc. are also used during this process. The
proposed framework can handle the production scheduling
problem with or without the condition of grade
uncertainty.Three different case studies have been carried
out using Particle Swarm Optimization (PSO), Bat Algorithm
(BA) and Differential Evolution (DE). The aim of these case
studies was to determine the capabilities and efficiency of
the proposed framework and of the respective metaheuristic
technique along with of their different variants. By making
comparison with the results obtained using CPLEX in terms of
computational time and solution quality it was learnt that
the proposed procedure can produce results of reasonable
quality in relatively shorter period of time with smaller
$\%$ gap and standard deviation.},
cin = {511410 / 510000},
ddc = {620},
cid = {$I:(DE-82)511410_20140620$ / $I:(DE-82)510000_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-rwth-2015-053066},
url = {https://publications.rwth-aachen.de/record/538444},
}
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