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@PHDTHESIS{Sattarvand:51125,
author = {Sattarvand, Javad},
othercontributors = {Niemann-Delius, Christian},
title = {{L}ong-term open-pit planning by ant colony optimization},
address = {Aachen},
publisher = {Publikationsserver der RWTH Aachen University},
reportid = {RWTH-CONV-113440},
pages = {XII, 130 S. : Ill., graph. Darst.},
year = {2009},
note = {Zusammenfassung in dt. und engl. Sprache. - Retraction. Aus
rechtlichen Gründen kann der Zugriff nicht gewährt werden},
abstract = {The problem of long-term planning of a hard rock open pit
mine (discontinuous exploitation operation) is a large
combinatorial problem which cannot be solved in a reasonable
amount of time through mathematical programming models
because of its large size. In this thesis, a new
metaheuristic algorithm has been developed based on the Ant
Colony Optimization (ACO) and its application in long-term
scheduling of a two dimensional hypothetical block model has
been analysed. ACO is inspired by the foraging behaviour of
ants (i.e. finding the shortest way from the colony to the
food source), and has been successfully implemented in
several combinatorial optimization problems. In nature, ants
transmit a message to other members by laying down a trail
with a chemical called pheromones. Instead of travelling in
a random manner, the pheromone trail allows the ants to
trace the path. Over time, the pheromones layed over longer
paths evaporate, whereas those over shorter routes continue
to be marched over. In order to simulate the ACO process for
long-term planning of a hard rock open-pit mine, various
programming variables have been considered for each block as
the pheromone trails. The number of these variables is equal
to the number of planning periods. In fact these pheromone
trails represent the desirability of the block for being the
deepest point of the mine in that column for the given
mining period. The shape of any given pit (in respect to the
slope angles) can be represented by means of a simple array
of integer numbers. Each element in this array shows the
depth of the pit in an individual column of block model.
Extending this concept to a long-term production planning, a
mine schedule would be represented by an array that has
several mine depths at each column of block model related to
different production periods. At the beginning, the values
of the pheromone trails are initialized according to a mine
schedule generated by Lerchs-Grossmann’s algorithm and the
alternative to parameterization algorithm of Wang $\&$
Sevim. During initialization, relatively higher values of
pheromones are assigned to those blocks that are close to
the deepest points of the push backs in the initial mine
schedule. This leads the procedure to construct a series of
random schedules which are not far from the initial
solution. In each ACO iteration, several mine schedules are
constructed based on current pheromone trails. This is
implemented through a process called “depth
determination”. In this process the depth of a mine in
each period is determined for each column of the block
model. The higher the value of the pheromone trail of a
particular block, the higher the possibility of selecting
that block as the pit depth in that period. Subsequently the
pheromone values of all blocks are reduced to a certain
percentage (evaporation) and additionally the pheromone
value of the participating blocks used in defining the
constructed schedules are increased according to the quality
of the generated solutions. Through repeated iterations, the
pheromone values of the blocks which define the shape of the
optimum solution are increased whereas those of the others
have been significantly evaporated. The ACO optimization
iterations could be implemented in a variety of ways. The
Ant System (AS) is the first and simplest method, whereby
all of the constructed schedules are allowed to contribute
in the pheromone deposition. In each iteration of the second
method, the Elitist Ant System (EAS), the best schedule
found up to that iteration (the best-so-far schedule) is
also allowed to deposit pheromones. ASrank is the third
method in which only a few good schedules are able to add
pheromones. The other variants are the Max-Min Ant System
(MMAS) and the Ant Colony System (ACS), which allow only the
best-so-far schedule to deposit pheromones and utilise
special pheromone limitations in order to prevent the
stagnation in local optimums. To test the efficiency of the
algorithm, a computer program has been developed in Visual
Basic 2005 programming language. As a case study, the block
model of a hypothetical iron ore deposit with 1000 blocks
was considered and different variants of ACO had been
analysed in order to find the best combination of ACO
parameters. The analysis revealed that the ACO is able to
improve the value of the initial mining schedule by up to
$34\%$ in a reasonable computational time. This is mainly
contributed to the consideration of the penalties to the
deviations of the capacities and the production qualities
from their permitted limits. It had also been proved that
the MMAS is the most explorative variant, while ACS is the
fastest method. These two variants also count as the only
variants which could be applied to a large block model in
respect to the amount of memory needed.},
keywords = {Tagebau (SWD) / Planung (SWD) / Ameisenalgorithmus (SWD) /
Optimierung (SWD)},
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-opus-26914},
url = {https://publications.rwth-aachen.de/record/51125},
}
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