In: Statistics and Probability
LG owns five production plants where 90" high definition televisions are produced. LG can sell up to 25,000 televisions per year at the price of $4000 per television. For each plant, the production capacity, the production cost per television, and the fixed cost of operating a plant for year are given below.
Production |
Plant |
Cost per |
|
Plant |
Capacity |
Fixed Cost |
Television |
1 |
10000 |
$9 million |
$1,600 |
2 |
8000 |
$5 million |
$2,000 |
3 |
9000 |
$3 million |
$2,300 |
4 |
7000 |
$4 million |
$2,100 |
5 |
6000 |
$1 million |
$2,400 |
a. Use Solver to determine how LG can maximize its yearly profit from television production.
b. Use SolverTable to determine how the optimal plant locations vary as the number of televisions that can be sold varies from 20,000 to 30,000 (in 1000 unit increments).
Plant | Production Capacity | Fixed cost | Variable cost per TV | Let | ||||||||||
1 | 10000 | $9,000,000.00 | $1600 | X1 | Number of TV manufactured in Plant 1 | |||||||||
2 | 8000 | $5,000,000.00 | $2000 | X2 | Number of TV manufactured in Plant 2 | |||||||||
3 | 9000 | $3,000,000.00 | $2300 | X3 | Number of TV manufactured in Plant 3 | |||||||||
4 | 7000 | $4,000,000.00 | $2100 | X4 | Number of TV manufactured in Plant 4 | |||||||||
5 | 6000 | $1,000,000.00 | $2400 | X5 | Number of TV manufactured in Plant 5 | |||||||||
Y1 | Binary Variable (1 if X1>0 else 0) | |||||||||||||
Y2 | Binary Variable (1 if X2>0 else 0) | |||||||||||||
Y3 | Binary Variable (1 if X3>0 else 0) | |||||||||||||
Y4 | Binary Variable (1 if X4>0 else 0) | |||||||||||||
Y5 | Binary Variable (1 if X5>0 else 0) | |||||||||||||
Objective Function | ||||||||||||||
Profit= | Number of TVs from each plant* Selling Price-Fixed Cost-Number of TVs from Each Plant * Variable cost in that plant location | |||||||||||||
Profit= | (X1+X2+X3+X4+X5)*4000 -( 9,000,000Y1+5,000,000Y2-3,000,000Y3-4,000,000Y4-1,000,000Y5)-(X1*1600-X2*2000-X3*2300-X4*2100-X5*2400) | |||||||||||||
Profit= | 4000X1+4000X2+4000X3+4000X4+4000X5-1600X1-2000X2-2300X3-2100X4-2400X5-(9,000,000Y1+5,000,000Y2-3,000,000Y3-4,000,000Y4-1,000,000Y5) | |||||||||||||
Z= | 2400X1+2000X2+1700X3+1900X4+1600X5-(9,000,000Y1+5,000,000Y2-3,000,000Y3-4,000,000Y4-1,000,000Y5) | |||||||||||||
Subject to | ||||||||||||||
Constraint 1 | ||||||||||||||
Maximum Sales can be 25000 | ||||||||||||||
X1+X2+X3+X4+X5<=25000 | ||||||||||||||
Constraint 1 | ||||||||||||||
Individual plant capacity constraint | ||||||||||||||
Plant 1: X1<=10000Y1 | ||||||||||||||
Plant 2: X2<=8000Y2 | ||||||||||||||
Plant 3: X3<=9000Y3 | ||||||||||||||
Plant 4: X4<=7000Y4 | ||||||||||||||
Plant 5: X5<=6000Y5 | ||||||||||||||
Non Negative constraint: | ||||||||||||||
X1,X2,X3,X4,X5>=0 | ||||||||||||||
Binary Variables | ||||||||||||||
Y1,Y2,Y3,Y4,Y5 are binary variables |
Selling Capacity |
X1 | X2 | X3 | X4 | X5 |
20000 | 0 | 8000 | 9000 | 0 | 3000 |
21000 | 0 | 8000 | 9000 | 0 | 4000 |
22000 | 9000 | 0 | 0 | 7000 | 6000 |
23000 | 10000 | 0 | 0 | 7000 | 6000 |
24000 | 10000 | 8000 | 0 | 0 | 6000 |
25000 | 10000 | 0 | 9000 | 0 | 6000 |
26000 | 9000 | 8000 | 9000 | 0 | 0 |
27000 | 10000 | 8000 | 9000 | 0 | 0 |
28000 | 10000 | 0 | 0 | 7000 | 6000 |
29000 | 0 | 8000 | 9000 | 6000 | 6000 |
30000 | 9000 | 8000 | 0 | 7000 | 6000 |
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