Table
1
Mining
and milling costs for each open pit mines
|
||||||||
Satellite
Pit
|
A
|
B
|
C
|
D
|
||||
Mining
cost
|
15
|
10
|
18
|
12
|
||||
Milling
cost
|
12
|
15
|
10
|
11
|
||||
Cu -concentrate/t ore
|
0.2
|
0.2
|
0.15
|
0.2
|
||||
Zn-concentrate/t ore
|
0.2
|
0.2
|
0.25
|
0.2
|
||||
Mb-concentrate/t ore
|
0.15
|
0.1
|
0.15
|
0.2
|
||||
Table
2: Production for each
satellite open pit Mines.
|
||||||||
satellite
Pit
|
minimum ore production(t)
|
Max .Ore Production
|
||||||
A
|
400
|
800
|
||||||
B
|
600
|
1000
|
||||||
C
|
500
|
1500
|
||||||
D
|
200
|
2000
|
||||||
Let Pit A = X1,
Pit B = X2, Pit C = X3, Pit D = X4
The linear
Model:
Cost
minimization; Z = 27X1 + 25x2
+ 28x3 + 23x4
Table3.
Maximization of Production and Minimization of
cost.
|
|||||||
Column1
|
X₁
|
X₂
|
X₃
|
X₄
|
Total
|
Column2
|
Limits
|
Production
|
400
|
600
|
500
|
1025
|
|||
Total Cost
|
27
|
25
|
28
|
23
|
63375
|
||
Cu-Conc/t Ore
|
0.2
|
0.2
|
0.15
|
0.2
|
480
|
≥
|
300
|
Zn-Conc/t Ore
|
0.2
|
0.2
|
0.25
|
0.2
|
530
|
≥
|
300
|
Mb-Conc/t Ore
|
0.15
|
0.1
|
0.15
|
0.2
|
400
|
≥
|
400
|
Total Conc.
|
0.55
|
0.5
|
0.55
|
0.6
|
1410
|
≤
|
2000
|
Pit A
|
1
|
0
|
0
|
0
|
400
|
≥
|
400
|
1
|
0
|
0
|
0
|
400
|
≤
|
800
|
|
Pit B
|
0
|
1
|
0
|
0
|
600
|
≥
|
600
|
0
|
1
|
0
|
0
|
600
|
≤
|
1000
|
|
Pit C
|
0
|
0
|
1
|
0
|
500
|
≥
|
500
|
0
|
0
|
1
|
0
|
500
|
≤
|
1500
|
|
Pit D
|
0
|
0
|
0
|
1
|
1025
|
≥
|
200
|
0
|
0
|
0
|
1
|
1025
|
≤
|
2000
|
Executive summary of the results for the model
by answering the following questions.
1. What are the optimum productions from each satellite pit and total
production per week
Optimum production for
each pit and total production are given in the table below.
Table4
Optimum Productions
|
Total production /week
|
|
Pit A
|
400
|
400
|
Pit B
|
600
|
600
|
Pit C
|
500
|
500
|
Pit D
|
1025
|
1025
|
2.
What will be the profit from sales per week?
Table5 The overall production for Marapana for each
ore concentrate :
|
|
ore
|
total production of each conc.(tonnes/week)
|
Cu-Conc/t Ore
|
480
|
Zn-Conc/t Ore
|
530
|
Mb-Conc/t Ore
|
400
|
The smelter’s payment
to Marapana Mining Ltd is; $300/t Cu-conc, $250/t Zn-conc and $550/t Mb.
Now, these amounts of
money are paid to the Mining Company so it becomes the company’s revenue. So
the revenue generated from sales per week is:
Z =
480x300 + 530x250 + 400x550 = $496,500.00/week.
From the LP model, the
total cost of mining and milling is $63,375/week.
Therefore, the profit
from the sales is:
Profit = Revenue – Cost = $496,500 - $63,375 =
$433,125/week.
3.
What changes are required from analyzing the results?
From the results
obtained from the analysis, it is required that the current production target
needs to be maintained at the lowest possible cost given above. In order to
achieve this objective, there are several things need to be changed as per
required by the analysis. One of them is to change aging trucks and salvage
them and replace with new ones. From experience, cost increases and high
production loss could occur with aging trucks. Since the production targets are
limited to some conditions which require almost exact values. There is a need
to have skilled labors to maintain the steady flow of production at low cost.
If there are unskilled labors then replace them with competitive and skilled
labors or if they are many then remove some in order to reduce cost.
4.
From the results of LP modeling using Solver(in excel), describe
the results and write an executive summary to Marapana Mining limited telling
about the optimum production levels being found by your LP modeling.
Table 6.
Answer report.
Objective Cell (Min)
|
|||||||||
Cell
|
Name
|
Original Value
|
Final Value
|
||||||
$F$4
|
Total Cost Total
|
0
|
63375
|
||||||
Variable Cells
|
|||||||||
Cell
|
Name
|
Original Value
|
Final Value
|
Integer
|
|||||
$B$3
|
Production
X₁
|
0
|
400
|
Contin
|
|||||
$C$3
|
Production
X₂
|
0
|
600
|
Contin
|
|||||
$D$3
|
Production
X₃
|
0
|
500
|
Contin
|
|||||
$E$3
|
Production
X₄
|
0
|
1025
|
Contin
|
|||||
Constraints
|
|||||||||
Cell
|
Name
|
Cell Value
|
Formula
|
Status
|
Slack
|
||||
$F$10
|
Pit A Total
|
400
|
$F$10<=$H$10
|
Not Binding
|
400
|
||||
$F$11
|
Pit B Total
|
600
|
$F$11>=$H$11
|
Binding
|
0
|
||||
$F$12
|
Pit B Total
|
600
|
$F$12<=$H$12
|
Not Binding
|
400
|
||||
$F$13
|
Pit C Total
|
500
|
$F$13>=$H$13
|
Binding
|
0
|
||||
$F$14
|
Pit C Total
|
500
|
$F$14<=$H$14
|
Not Binding
|
1000
|
||||
$F$15
|
Pit D Total
|
1025
|
$F$15>=$H$15
|
Not Binding
|
825
|
||||
$F$16
|
Pit D Total
|
1025
|
$F$16<=$H$16
|
Not Binding
|
975
|
||||
$F$5
|
Cu.Conc/(t) Ore Total
|
480
|
$F$5>=$H$5
|
Not Binding
|
180
|
||||
$F$6
|
Zn-Conc/(t) Ore Total
|
530
|
$F$6>=$H$6
|
Not Binding
|
230
|
||||
$F$7
|
Mb-Conc/(t) Ore Total
|
400
|
$F$7>=$H$7
|
Binding
|
0
|
||||
$F$8
|
Total Conc. Total
|
1410
|
$F$8<=$H$8
|
Not Binding
|
590
|
||||
$F$9
|
Pit A Total
|
400
|
$F$9>=$H$9
|
Binding
|
0
|
||||
Answer report is in
three parts: 1.Objective cell (Min), 2. Variable cells and 3. Constraints cell.
Objective cell gives the optimal value in the final value column. In the
Variable cells in the final value column, optimal values are also given. The
constraints cells shows limits generated by solver on the right-hand side and
shown on the left-hand side is the difference between the entered solver
generated limit values. The slack column shows the amount of slack for each
constraint which indicates there is flexibility exists. For example, removal or
addition of one haulage truck from the total fleet size will affect the
production or cost per week.
Table7
Sensitivity Report.
Variable Cells
|
||||||||||
Final
|
Reduced
|
Objective
|
Allowable
|
Allowable
|
||||||
Cell
|
Name
|
Value
|
Cost
|
Coefficient
|
Increase
|
Decrease
|
||||
$B$3
|
Production
X₁
|
400
|
0
|
27
|
1E+30
|
9.75
|
||||
$C$3
|
Production
X₂
|
600
|
0
|
25
|
1E+30
|
13.5
|
||||
$D$3
|
Production
X₃
|
500
|
0
|
28
|
1E+30
|
10.75
|
||||
$E$3
|
Production
X₄
|
1025
|
0
|
23
|
13
|
23
|
||||
Constraints
|
||||||||||
Final
|
Shadow
|
Constraint
|
Allowable
|
Allowable
|
||||||
Cell
|
Name
|
Value
|
Price
|
R.H. Side
|
Increase
|
Decrease
|
||||
$F$10
|
Pit A Total
|
400
|
0
|
800
|
1E+30
|
400
|
||||
$F$11
|
Pit B Total
|
600
|
13.5
|
600
|
400
|
600
|
||||
$F$12
|
Pit B Total
|
600
|
0
|
1000
|
1E+30
|
400
|
||||
$F$13
|
Pit C Total
|
500
|
10.75
|
500
|
1000
|
500
|
||||
$F$14
|
Pit C Total
|
500
|
0
|
1500
|
1E+30
|
1000
|
||||
$F$15
|
Pit D Total
|
1025
|
0
|
200
|
825
|
1E+30
|
||||
$F$16
|
Pit D Total
|
1025
|
0
|
2000
|
1E+30
|
975
|
||||
$F$5
|
Cu.Conc/(t) Ore Total
|
480
|
0
|
300
|
180
|
1E+30
|
||||
$F$6
|
Zn-Conc/(t) Ore Total
|
530
|
0
|
300
|
230
|
1E+30
|
||||
$F$7
|
Mb-Conc/(t) Ore Total
|
400
|
115
|
400
|
195
|
165
|
||||
$F$8
|
Total Conc. Total
|
1410
|
0
|
2000
|
1E+30
|
590
|
||||
$F$9
|
Pit A Total
|
400
|
9.75
|
400
|
400
|
400
|
||||
Sensitivity report above contains some relevant information regarding the
effect of changes to either an objective function coefficient or right hand
side as discussed in the answer report. The variable cells section includes the
reduced cost and ranges of optimality for the objective function coefficient
(expressed in terms of allowable increases and allowable decreases). The
constraints section contains the shadow prices and ranges of feasibility for
right hand side values (again expressed in allowable increases and allowable decreases).
This is linear programming equivalent of marginal analysis in economics, as the
results deal with the effects of making one parameter change to the model.
The value “1E+30” (for
allowable increase and decrease) is an excel’s way of saying infinity.
Management Report.
The above results are used to produce a management report for Marapana
Mining Ltd. Practically; these results are presented as attachments to the
actual report to the Mine Superintendent. LP modeling was done by Mining &
Mineral Processing Student Consulting Association..
Date: 9th May 2018.
To: Kuiwatinga, Marapana Open Pit Mine
Production Manager.
From: Mining
& Mineral Processing Student Consulting Group Associates.
Subject: Optimization of Production per week for Marapana’s
Mining Ltd Four Satellite Open Pits.
The Marapana Open Pit
Mine Manager wants to maximize the production levels and minimize cost as much
as possible in order to expect a maximum return per week. It is understood the
management wants to optimize returns by optimizing number of haulage fleet,
drivers and mechanics to reduce cost and maximize production. The cost is
minimized to about $63 375/week which places constraints on the operating fleet
of trucks, drivers and mechanics.
There is no exact
recommendation made in this analysis as there were limited raw information such
as haulage fleet size, drivers, mechanics and other parameters necessary for
maximum production at lowest possible cost. But with the limited information
given, we only recommend few. It is recommended the available resources must be
utilized so as to keep the current production is maintained at a constant rate.
Whenever the equipment needs maintenance then do so as soon as possible or for
worn out fleets, they need to be replaced with new fleet size so that
continuous flow of production is maintained throughout the week and even
throughout the mine life if it is required. Not only that but also, labors must
given special consideration because they
are the ones that will make things happen so competitive, skilled and required
number of labors are necessary to achieve required production targets.
If our recommendations
and work quality solve your Management’s problems, then do not hesitate to
engage us for such services we provide.
Yours truly
…………………………………
Mara Hawks
Managing Director
Mining & Mineral
Processing Student Consulting Group Associates.
Related Articles:
Mine Management Questions and Answers Series (1)
Mine Management Questions and Answers Series (2)
Mine Management Questions and Answers Series (3)
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