1
MINING SCHEDULE OPTIMISATION FOR CONDITIONALLY SIMULATED
OREBODIES.
Merab Menabde, Gary Froyland, Peter Stone, and Gavin Yeates.
BHP Billiton, 180 Lonsdale St, Melbourne VIC 3000,
GPO Box 86A, Melbourne VIC 3001
Traditionally the process of mine devel
opment and design and long

term scheduling
is based on a deterministic orebody model built by the spatial interpolation of
drillhole data using some kind of Kriging procedure. Typical steps in mine design
would include the development of a number of minin
g phases (pushbacks) and a life

of

mine schedule maximising the mine’s net present value (NPV).
A number of software packages that deal with some or all of these issues are
commercially available and widely used in the mining industry. All of them treat
the
above described process in a strictly deterministic way. In reality, however, the
drillhole data is usually too sparse to support a unique deterministic block model.
This paper describes work undertaken by the Exploration and Mining Technology
Group
within BHP Billiton to develop a new mathematical algorithm for mine
optimisation under uncertainty, based upon a number of conditionally simulated ore
body models. This algorithm is implemented in a new software package. The
software uses a number of pro
prietary algorithms and the commercially available
mixed integer programming package ILOG CPLEX. We target all phases of mine
optimisation, including the NPV optimal block extraction sequence, pushback design,
and simultaneous cutoff grade and mining sche
dule optimisation.
2
1.
Introduction
The paper describes the development and implementation of an open pit mine
scheduling software package based on the mixed integer programming model and
conditionally simulated multiple realisations of the ore body blo
ck model.
Traditionally open pit mine planning and design is based on a block model of the
ore body built by using some kind of interpolation techniques, such as Kriging, from
the drillhole sample data. This single model is assumed to be a fair represent
ation of
reality and is used for mine design and optimisation. The design process consists of 3
main steps: (a) finding the block extraction sequence which produces the best net
present value (NPV) whilst satisfying the geotechnical slope constraints, (b)
designing
the practically minable mine phases (pushbacks) which are roughly based on the
optimal block sequence, and (c) optimising the mining schedule and cutoff grades
(COG). The NPV of this “optimal” schedule is considered as a main criterion of the
eco
nomical viability of the project.
However, in reality there are many uncertainties in the models and parameters
used in optimisation, which make the adoption of a single economic criterion for a
project very questionable. One of the most important sources
of uncertainty is the
block model itself. The drillhole data is typically too sparse to support a unique and
deterministic block model. A more realistic approach is to use conditional simulation
techniques (see Dimitrakopoulos (1997) and references there
in) which allow the
generation of a number of equally probable realisations of the block model, all of
them honouring the drillhole data and the first and second order statistics of the ore
body represented, respectively, by the probability distribution an
d variogram (e.g.
Isaaks and Srivastava, 1989). The simplest and most straightforward use of this set of
block model is to estimate the variability in the project NPV associated with the
orebody uncertainty by valuing the “optimal” schedule obtained from t
he kriged
model through each of the conditionally simulated realisations. The more interesting
question is whether is it possible to use the set of conditional simulations to get a
better mine design and production schedule. By “better” we mean here a high
er
expected NPV (which becomes a random variable in case of multiple realisations of
the ore body model) and/or less variability from one realisation to other (i.e. lower
variance of NPV). A new promising approach to this problem is currently being
3
develop
ed at the WH Bryan Research Centre at the University of Qeensland
(Ramazan and Dimitrakopoulos, 2003).
In this paper we address one particular aspect of the optimisation under
uncertainty, namely the simultaneous optimisation of the extraction sequence an
d
COG. The importance of using optimal (variable) COG has been known to the mining
community for a long time (e.g. Lane, 1988). It will be demonstrated here that the use
of variable COG optimised using the set of equiprobable block models can provide a
sub
stantial improvement in terms of expected NPV. We use an approach based on the
mixed integer programming technique which can provide a truly optimal schedule, as
opposed to various heuristic methods used in most of the commercially available
mining optimi
sation software packages.
2.
Mining schedule optimisation as a mixed integer programming model.
Typically, the ore body block model contains between 50,000 to 500,000 blocks,
which must be scheduled over a period of 15

25 years. The objective of any
sch
eduling procedure is to find the block extraction sequence, which produces the
maximum possible net present value (NPV) and obeys a number of constraints. The
latter include:
(a)
geotechnical slope constraints which are modelled by a set of precedence arcs
b
etween individual blocks;
(b)
mining constraints, i.e. total maximum amount of rock which can be mined in
one time period (usually 1 year);
(c)
processing constraints, i.e. maximum amount or ore which can be processed
through a given processing plant in one time p
eriod;
(d)
and the market constraints, i.e. the maximum amount of metal, which can be
sold in one time period;
The mathematical formulation of the scheduling procedure in terms of binary
decision variables describing in which period the particular block is ext
racted and
what is its destination (either processing plant or waste dump) is quite
straightforward. The size of the problem is, however, prohibitively large. Apart from
the computational difficulties, the hypothetical optimal block extraction sequence may
4
be completely impractical due to the requirements for the mining equipment access
and relocation.
Because of these problems the mine scheduling is done using much bigger
elementary units which are typically aggregations of hundreds or even thousands of
bl
ocks. The aggregation of blocks is a nontrivial problem. For example, simply
combining rectangular blocks into a larger rectangular block with dimensions
multiples of that of individual blocks can effectively reduce the size of the problem
but will provide
a very poor approximation for the geotechnical slopes.
We have recently developed a new algorithm for block aggregation, which
preserves the slope constraints, and is very flexible allowing the user to fully control
the size and shape of these aggregati
ons. The details of this algorithm will not be
discussed here. The optimisation procedure, however, can be applied to any
aggregation of block with a set of precedence arcs, prescribing which blocks should
be extracted before the given one. As an example w
e consider here the scheduling of
mining phases.
In practice, the open mine is divided into a number of mining phases, which
are mined bench by bench, each bench represented by a horisontal layer of blocks
within the given mining phase and having the sam
e elevation. A bench within a
mining phase is sometimes refered to as a “panel”. The mining phases can be mined
one by one from top to bottom, however this kind of schedule is usually suboptimal.
Mining several phases simultaneously and applying variable C
OG can produce much
better results. There are several commercially available packages, which use
proprietary (and undisclosed) heuristics to optimise the schedule and COG. It is
difficult to estimate their effectiveness as the upper theoretical limit on NP
V remains
unknown. Besides, these methods cannot be directly used for a set of conditionally
simulated ore body models.
The standard optimisation technique widely used in many industrial
applications is the linear and integer programming (e.g. Padberg, 20
03). The main
difficulty in its application to mining scheduling is that the optimisation with variable
COG in its direct formulation leads to a nonlinear problem, which is much harder to
solve. Our approach provides an effective linearisation of this prob
lem, making it
possible to use a mixed integer programming (MIP) formulation for a simultaneous
optimisation of the extraction sequence and COG for a number of conditionally
simulated orebody models. The MIP formulation we use here is similar to the one
5
u
sed by Cacceta (2002) but is generalised to include the multiple realisations of
conditional simulations and variable cutoff grades. This approach also allows one to
estimate the gap between the obtained solution and the upper theoretical limit.
We consi
der the simplest case when we have one rock type containing one
metal type, which can be processed through one processing plant. Generalisation to
the case of multiple rock types, metals, and processing streams is cumbersome but
straightforward. For simpli
city we consider here only the case of a discrete set of
COGs, though it is possible to generalise the results to the continuous COG case. We
use the following notations:
T
is the number of scheduling periods;
N
is the number of simulations;
P
is the
total number of panels;
G
is the number of all possible cutoff grades;
n
i
R
is the total rock in the panel
i
in simulations
n
.
n
ij
Q
is the total ore in the panel
i
, simulation
n
, when mined with the COG
j
;
n
ij
V
is the value of the panel
i
, simulation
n
,
when mined and processed with
the COG
j
;
0
t
R
is the maximum mining capacity in period
t
;
0
t
Q
is the maximum processing rate in period
t
;
i
S
is the set of panels that must be removed before starting the panel
i
;
d
t
is the time discount factor;
ijt
x
is the fraction of the panel
i
is
extracted with the COG
j
in period
t
;
it
y
is a binary variable equal
to 1 if the extraction of the panel
i
has started in
periods 1 to
t
, and equal to 0 othewise;
jt
is a binary variable controlling the selection of the COG applied in period
t
;
The MIP formulation is:
N
n
P
i
t
ijt
G
j
n
ij
d
x
V
N
Maximise
1
1
1
1
(1)
subject
to the following constraints:
6
t
all
for
R
x
R
N
N
n
t
P
i
ijt
G
j
n
i
,
1
1
0
1
1
(2)
t
all
for
Q
x
V
N
N
n
t
ijt
P
i
G
j
n
ij
,
1
1
0
1
1
(3)
t
and
i
all
for
y
y
it
t
i
,
1
,
(4)
t
G
j
it
ij
i
all
for
y
x
1
1
,
(5)
i
S
k
G
j
t
kj
it
t
and
i
all
for
x
y
1
1
,
(6)
t
all
for
G
j
jt
,
1
1
(7)
t
and
j
i
all
for
x
jt
ijt
,
,
,
(8)
The objective function (1) re
presents the discounted cash flow. Constraints (2) and (3)
enforce the mining and processing limits on average. Constraints (4)
–
(6) enforce the
panel extraction precedence constraints, and constraints (7) and (8) ensure that the
same COG is applied to al
l panels extracted in any given time period.
This MIP formulation is solved by the commercially available software package
CPLEX version 9.0, by ILOG Inc.
3.
Case study
To test the algorithm we have chosen 10 conditional simulations of a block model
contai
nting one type of metal and using one processing plant. Because of
confidentiality requirements all the economic parameters were rescaled and do not
represent reality. However, all the relative characteristics which demonstrate the
potential of the new m
ethod are not affected by the rescaling. The ultimate pit for the
design was chosen by using the Lersch

Grossman algorithm (Lersch and Grossman,
1965) and the procedure similar to that used in Whittle Four

X software. The ultimate
pit contains 191 million
tonnes of rock and 62.9
2.7 million tonnes of ore (above
the marginal COG = 0.6 %). The undiscounted value in the ultimate pit (if processed
with the marginal COG) is $ (1,316
99) million. It was divided into 6 mining phases
and scheduled over 12 ye
ars. The mining rate was set to 30 MT/year and the
processing rate to 5 MT/year. The initial capital investment was assumed to be $300
million, and the discount rate 10%. The base case optimisation was done using the
7
marginal COG and produced the discount
ed cash flow $(704
31) million, and the
NPV was $(404
31) million. The mining schedule and the NPV are shown,
respectively, in Fig. 1 and 2. The second optimisation was done using the variable
COG, but was based on the mean grade block model, i.e. it w
as similar to the one
which can be generated by using one deterministic model. The schedule was then
evaluated against all 10 realisations of ore body model and produced the NPV =
$(485
40) million, an increase of 20 % over the base case. The results ar
e shown in
Fig. 3 and 4. The third optimisation was done using the algorithm described in section
2, and produced the NPV = $(505
43) million, a further increase of 4.1% over the
case of mean grade based optimisation. The results are shown in Fig. 5 and
6. The
relative variability of NPV in all cases was roughly the same, about 8%. Another
important result of the variable COG policy is that the pay

back period (defined here
as the time when the cummulative NPV becomes equal to zero) is decreased from 5
to
3 years.
The increase of 4.1% in NPV may be not seen as a very substantial, but it should
be mentioned that the block model considered does not have a high variability. The
relative variance in the undiscounted value of the ultimate pit is only 7.6%.
There are
many deposits which can have variability of the order of 20
–
30%. For these kind of
deposits the potential improvement in the expected NPV may be substantially high.
4.
Conclusions
We have developed a new method for simulataneous optimisatio
n of the extraction
sequence and cutoff grade policy for a set of conditionally simulated orebody models.
This method is based on the mixed integer programming model and uses the
commercially available software package CPLEX by ILOG Inc. The goal of the
op
timisation is to find the extraction sequence and cutoff grade policy, which, when
evaluated through the whole set of conditionally simulated orebodies, whill produce
the best possible expected NPV. The degree of accuracy of this optimised schedule
can be
estimated precisely, in contrast to a number of heuristic routines used in
mining optimisation software packages. A fully functional software prototype that
uses the new optimisation method has been developed.
8
In this study we were using the expected N
PV as the objective function and
the mining and processing contraints were applied to the mean rock and ore tonnages.
Some of the possible extensions of this method may include some kind of penalty
functions in the objective function in order to find a sch
edule with a reduced
variability in NPV, defining hard constraints bounding the NPV from below, or
defining a lower bound on the annual cash flows. Another very interesting
generalisation may include a stochastic price model for metals and adjustable cuto
ff
grade policy.
9
References.
Caccetta, L and Hill S P, 2003. An application of branch and cut to open pit mine
scheduling, Journal of Global Optimization, 27:349

365.
Dimitrakopoulos, R, 1977. Conditional Simulations: Tools for Modelling Uncertain
ty
in Open Pit Optimisation, Proceedings of the 1997 Whittle Conference “Optimizing
with Whittle”, 31
–
42.
Isaaks, E H and Srivastava, R M, 1989. Applied Geostatistics, Oxford Univ. Press,
NY, 561 p.
Lane K, F, 1988. The Economic Definition of Ore, Mini
ng Journal Books Ltd,
London, 147 p.
Lerchs, H and Grossman, L, 1965. Optimum Design of Open

Pit Mines, Trans. CIM,
LXVII, 17
–
24.
Padberg, M W, 1995. Linear optimization and extensions, Springer, NY, 449 p.
Ramazan, S and Dimitrakopoulos, R, 2003.
Stochastic integer programming based
modelling for long

term production scheduling of open pit mines. ARC linkage
project report N

6002

1. WH Bryan Mining Geology Research Centre, The
University of Queensland, Brisbane.
10
Figure Captions.
Figure 1 . M
ining schedule optimised with the marginal COG.
Figure 2. NPV of the schedule optimised with the marginal COG.
Figure 3. Mining schedule optimised with the mean grade model.
Figure 4. NPV of the schedule optimised with the mean grade model.
Figure 5. Minin
g schedule optimised with the set of conditional simulations.
Figure 6. NPV of the schedule optimised with the set of conditional simulations.
11
Figure 1.
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
1
2
3
4
5
6
7
8
9
10
11
12
Periods
Rock (tonnes)
12
Figure 2.
$400,000,000
$300,000,000
$200,000,000
$100,000,000
$0
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
1
2
3
4
5
6
7
8
9
10
11
12
Periods
NPV
13
Figure 3
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
1
2
3
4
5
6
7
8
9
10
11
Period
Rock (tonnes)
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Cutoff Grade (%)
14
Figure 4
$400,000,000
$300,000,000
$200,000,000
$100,000,000
$0
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
1
2
3
4
5
6
7
8
9
10
11
Periods
NPV
15
Figure 5
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
1
2
3
4
5
6
7
8
9
10
11
Periods
Rock (tonnes)
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Cutoff Grade (%)
16
Figure 6
$400,000,000
$300,000,000
$200,000,000
$100,000,000
$0
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
1
2
3
4
5
6
7
8
9
10
11
Periods
NPV
17
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