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Transportation
Modeling
Problem

10/13/13

1
THE PROBLEM
The Epsilon Computers Co. sells desktop
computers to universities along University
belt, and ship them from three distribution
warehouses. The firm is able to supply the
following numbers of desktop computers to
the universities by the beginning of the
academic year:
10/13/13

2
Distribution
warehouse

Supply

Sta. Mesa
Taft Ave
Divisoria

150
200
50

total

400

Universities have ordered desktop computers that must be
delivered and installed by the beginning of the academic year:
University/College

Demand

CEU
FEU
UE

100
80
220

total

400
10/13/13

3
The shipping cost per desktop computer from each distributor to
each university are as follows:
To
From
1(Sta. Mesa)
2 (Taft Ave)
3 (Divisoria)

A
B
(CEU) (FEU)
7
10
6

5
12
3

C
(UE)
9
10
4

With cost minimization as criterion, Epsilon Company wants to determine
how many desktop computers should be shipped from each warehouse to
each university. Compare alternatives using,
a.Northwest Corner Rule (NCR)
b. Least Cost Method (LCM)
c. Stepping Stone Method
d. Vogel’s Approximation Method (VAM)
10/13/13

4
From

To

1
(STA. MESA)
2
(TAFT AVE)
3
(DIVISORIA)
Demand

10/13/13

A (CEU)

B (FEU)

X1a

7
X1b

5

100

9150

12
X2b

10

X3b
80

200

14

50

X2c
3

6
X3a

Supply

X1c

10
X2a

C (UE)

X3c
220

400

5
Objective Function:

C= cost of shipment of all
Xij= no. of computerd delivered
i= origin ; j= destination

Minimize: C= 7X1a + 5X1b + 9X1c + 10X2a + 12X2b + 10X2c + 6X3a + 3X3b + 14 X3c
Constraints:

10/13/13

X1a +X1b + X1c= 150
X2a + X2b + X2c = 200
X3a + X3b + X3c = 50
X1a + X2a + X3a= 100
X1b + X2b + X3b= 80
X1c+ X2c + x3c= 220
Xij ≥ 0
6
From

To

A (CEU)

B (FEU)
7

1
(STA. MESA)

100

Supply

5

9150

12

10

50
10

2

30

(TAFT AVE)

100

80

200

170
3

6

3
(DIVISORIA)
Demand

C (UE)

50
220

14

50

400

C= 700 + 250 + 360 + 1700 + 700 = P 3710
10/13/13

7
From

To

A (CEU)

B (FEU)

100

7 30

5 20

2
(TAFT AVE)

10

12

3
(DIVISORIA)

6

1
(STA. MESA)

Demand

100

LEGEND:
10/13/13

12345

50
80

C (UE)

Supply

9150
200

10
14

3

220

200

50

400

C = P 3190
8
From

To

A (CEU)

B (FEU)
7

1
(STA. MESA)

100

5

9150

12

10

10

30

(TAFT AVE)

100

80

200

170
3

6

3
(DIVISORIA)

10/13/13

Supply

50

2

Demand

C (UE)

50
220

Table derived through Northwest Corner
Rule

14

50

400

9
Compute for Improvement Indices:
Improvement Index- the increase/decrease in total cost that
would result from reallocating one unit to an unused square.
Unused
Square

Closed Path

Improvement
Indices

X1c

+X1c-X1b+X2b-X2c

+9-5+12-10= +6

X2a

+X2a-X1a+X1b-X2b

+10-7+5-12= -4

X3a

+X3a-X1a+X1b-X2b+X2c-X3c

+6-7+5-12+10-14= -12

X3b

+X3b-X2b+X2c-X3c

+3-12+10-14= -13
10/13/13

10
From

To

A (CEU)

B (FEU)
7

1
(STA. MESA)

100

5

9150

12

10

10

30

(TAFT AVE)

100

80

200

170
3

6

3
(DIVISORIA)

10/13/13

Supply

50

2

Demand

C (UE)

50
220

14

50

400

11
From

To

A (CEU)

B (FEU)
7

1
(STA. MESA)

100

C (UE)
5

9150

12

10

200

14

50

50
10

2
(TAFT AVE)

6

3
(DIVISORIA)
Demand

Supply

100

30
80

3

200
20
220

400

Improved Transportation Tableau @ C= P 3,320
10/13/13

12
NOTE: Test the solution for improvement by computing again the
improvement indices of the improved transportation tableau. If all indices
are positive then an optimal solution has been obtained, otherwise, repeat
the same steps for SSM.

Unused
Square

Closed Path

Improvement
Indices

X1c

+X1c-X1b+X3b-X3c

+9-5+3-14= -7

X2a

+X2a-X1a+X1b-X3b+X3c-X2b

+10-7+5-3+14-10= +9

X2b

+X2b-X2c+X3c-X3b

+12-10+14-3= +13

X3a

+X3a-X1a+X1b-X3b

+6-7+5-3= +1

10/13/13

13
From

To

A (CEU)

B (FEU)
7

1
(STA. MESA)

100

C (UE)
5

9150

12

10

200

14

50

50
10

2
(TAFT AVE)

6

3
(DIVISORIA)
Demand

Supply

100

30
80

3

200
20
220

400

Previously improved transportation tableau
10/13/13

14
From

To

A (CEU)

B (FEU)
7

1
(STA. MESA)

100

C (UE)
5

30
12

10

200

14

50

200

(TAFT AVE)
6

3
(DIVISORIA)
Demand

9150

20

10

2

Supply

100

50
80

3

220

400

New improved Transportation Tableau @ C= P 3180
10/13/13

15
Again, computing for improvement indices:
Unused
Square
X2a

+X2a-X1a+X1c-X2c

Improvement
Indices
+10-7+9-10= +2

X2b

+X2b-X1b+X1c-X2c

+12-5+9-10= +6

X3a

+X3a-X1a+X1b-X3b

+6-7+5-3= +1

X3c

+X3c-X3b+X1b-X1c

+14-3+5-9= +7

Closed Path

Since all improvement indices are all positive, optimal solution
is obtained @ C= P 3,180.
10/13/13

16
This method is an algorithm that finds an initial feasible
solution to transportation problem by considering the
“penalty cost” of not choosing cheapest available route.
Opportunity Cost- the cost of opportunities that are
sacrificed in order to take a specific action.

10/13/13

17
1. For each row with an available supply and each column with an unfilled
demand, calculate an opportunity/penalty cost by subtracting the smallest
cost per unit entry from the second smallest entry for a minimization
problem. While, for maximization problem, opportunity cost is calculated
by getting the difference between the highest and the second highest
entry.
2. Identify the row or column with largest opportunity(difference) cost.
3. Allocate maximum amount possible to the available route with the lowest
cost for minimization or highest revenue for maximization in the
row/column selected from step 2.
4.Reduce appropriate supply and demand by the amount allocated in step 3.
5. Remove any rows with zero available supply and columns with unfilled
demand for further consideration.
6.Return to step 1.
10/13/13

18
From

To

A (CEU)

7

1
(STA. MESA)

X1a

2
(TAFT AVE)
3
(DIVISORIA)
Demand

B (FEU)

C (UE)
5

X1b
12
X2b

100

10

X3b
80

200

14

50

X2c
3

6
X3a

9 150
X1c

10
X2a

Supply

X3c
220

400

Original Transportation Tableau
10/13/13

19
OPPORTUNITY COST FOR THE FIRST ALLOCATION
Row/ Column

Lowest Cost

Opportunity
Cost

1

7

5

2

2

10

10

0

3

6

3

3

A

7

6

1

B

5

3

2

C

10/13/13

Second
Lowest Cost

10

9

1

20
From

To

A (CEU)

B (FEU)

C (UE)

Supply

7

5

9150

2

10

12

10

200

(TAFT AVE)
3
(DIVISORIA)

6

14

50

1
(STA. MESA)

Demand

100

50 3
80

220

400

First allocation
10/13/13

21
OPPORTUNITY COST FOR THE SECOND ALLOCATION
Row/ Column

Lowest Cost

Opportunity
Cost

1

7

5

2

2

10

10

0

A

10

7

1

B

12

5

7

C

10/13/13

Second
Lowest Cost

10

9

1

22
From

To

A (CEU)

B (FEU)
7

1
(STA. MESA)

Supply

5

9150

12

10

200

14

50

30

2

10

(TAFT AVE)
3
(DIVISORIA)

6

Demand

C (UE)

100

50 3
80

220

400

Second allocation
10/13/13

23
OPPORTUNITY COST FOR THE THIRD ALLOCATION
Row/ Column

Lowest Cost

Opportunity
Cost

1

9

7

2

2

10

10

0

A

10

7

1

C

10/13/13

Second
Lowest Cost

10

9

1

24
From

To

1
(STA. MESA)

A (CEU)

B (FEU)
7

100

30

(TAFT AVE)
3
(DIVISORIA)

6

12

80

220

10

200

14

200

50 3

Third allocation
10/13/13

9150
20

10

100

Supply

5

2

Demand

C (UE)

50

400
C= P 3180
25
• DEGENERATE PROBLEMS
>> (column + row) – 1 , SSM not applicable
>> put zero (0) to any unused square and
proceed with SSM steps
• UNBALANCED PROBLEMS
>> demand ≠ supply
>> add dummy column/row
10/13/13

26
Otsukaresamadeshita!!!
Thank You for listening!!! 
10/13/13

27

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Transportation problem

  • 2. THE PROBLEM The Epsilon Computers Co. sells desktop computers to universities along University belt, and ship them from three distribution warehouses. The firm is able to supply the following numbers of desktop computers to the universities by the beginning of the academic year: 10/13/13 2
  • 3. Distribution warehouse Supply Sta. Mesa Taft Ave Divisoria 150 200 50 total 400 Universities have ordered desktop computers that must be delivered and installed by the beginning of the academic year: University/College Demand CEU FEU UE 100 80 220 total 400 10/13/13 3
  • 4. The shipping cost per desktop computer from each distributor to each university are as follows: To From 1(Sta. Mesa) 2 (Taft Ave) 3 (Divisoria) A B (CEU) (FEU) 7 10 6 5 12 3 C (UE) 9 10 4 With cost minimization as criterion, Epsilon Company wants to determine how many desktop computers should be shipped from each warehouse to each university. Compare alternatives using, a.Northwest Corner Rule (NCR) b. Least Cost Method (LCM) c. Stepping Stone Method d. Vogel’s Approximation Method (VAM) 10/13/13 4
  • 5. From To 1 (STA. MESA) 2 (TAFT AVE) 3 (DIVISORIA) Demand 10/13/13 A (CEU) B (FEU) X1a 7 X1b 5 100 9150 12 X2b 10 X3b 80 200 14 50 X2c 3 6 X3a Supply X1c 10 X2a C (UE) X3c 220 400 5
  • 6. Objective Function: C= cost of shipment of all Xij= no. of computerd delivered i= origin ; j= destination Minimize: C= 7X1a + 5X1b + 9X1c + 10X2a + 12X2b + 10X2c + 6X3a + 3X3b + 14 X3c Constraints: 10/13/13 X1a +X1b + X1c= 150 X2a + X2b + X2c = 200 X3a + X3b + X3c = 50 X1a + X2a + X3a= 100 X1b + X2b + X3b= 80 X1c+ X2c + x3c= 220 Xij ≥ 0 6
  • 7. From To A (CEU) B (FEU) 7 1 (STA. MESA) 100 Supply 5 9150 12 10 50 10 2 30 (TAFT AVE) 100 80 200 170 3 6 3 (DIVISORIA) Demand C (UE) 50 220 14 50 400 C= 700 + 250 + 360 + 1700 + 700 = P 3710 10/13/13 7
  • 8. From To A (CEU) B (FEU) 100 7 30 5 20 2 (TAFT AVE) 10 12 3 (DIVISORIA) 6 1 (STA. MESA) Demand 100 LEGEND: 10/13/13 12345 50 80 C (UE) Supply 9150 200 10 14 3 220 200 50 400 C = P 3190 8
  • 9. From To A (CEU) B (FEU) 7 1 (STA. MESA) 100 5 9150 12 10 10 30 (TAFT AVE) 100 80 200 170 3 6 3 (DIVISORIA) 10/13/13 Supply 50 2 Demand C (UE) 50 220 Table derived through Northwest Corner Rule 14 50 400 9
  • 10. Compute for Improvement Indices: Improvement Index- the increase/decrease in total cost that would result from reallocating one unit to an unused square. Unused Square Closed Path Improvement Indices X1c +X1c-X1b+X2b-X2c +9-5+12-10= +6 X2a +X2a-X1a+X1b-X2b +10-7+5-12= -4 X3a +X3a-X1a+X1b-X2b+X2c-X3c +6-7+5-12+10-14= -12 X3b +X3b-X2b+X2c-X3c +3-12+10-14= -13 10/13/13 10
  • 11. From To A (CEU) B (FEU) 7 1 (STA. MESA) 100 5 9150 12 10 10 30 (TAFT AVE) 100 80 200 170 3 6 3 (DIVISORIA) 10/13/13 Supply 50 2 Demand C (UE) 50 220 14 50 400 11
  • 12. From To A (CEU) B (FEU) 7 1 (STA. MESA) 100 C (UE) 5 9150 12 10 200 14 50 50 10 2 (TAFT AVE) 6 3 (DIVISORIA) Demand Supply 100 30 80 3 200 20 220 400 Improved Transportation Tableau @ C= P 3,320 10/13/13 12
  • 13. NOTE: Test the solution for improvement by computing again the improvement indices of the improved transportation tableau. If all indices are positive then an optimal solution has been obtained, otherwise, repeat the same steps for SSM. Unused Square Closed Path Improvement Indices X1c +X1c-X1b+X3b-X3c +9-5+3-14= -7 X2a +X2a-X1a+X1b-X3b+X3c-X2b +10-7+5-3+14-10= +9 X2b +X2b-X2c+X3c-X3b +12-10+14-3= +13 X3a +X3a-X1a+X1b-X3b +6-7+5-3= +1 10/13/13 13
  • 14. From To A (CEU) B (FEU) 7 1 (STA. MESA) 100 C (UE) 5 9150 12 10 200 14 50 50 10 2 (TAFT AVE) 6 3 (DIVISORIA) Demand Supply 100 30 80 3 200 20 220 400 Previously improved transportation tableau 10/13/13 14
  • 15. From To A (CEU) B (FEU) 7 1 (STA. MESA) 100 C (UE) 5 30 12 10 200 14 50 200 (TAFT AVE) 6 3 (DIVISORIA) Demand 9150 20 10 2 Supply 100 50 80 3 220 400 New improved Transportation Tableau @ C= P 3180 10/13/13 15
  • 16. Again, computing for improvement indices: Unused Square X2a +X2a-X1a+X1c-X2c Improvement Indices +10-7+9-10= +2 X2b +X2b-X1b+X1c-X2c +12-5+9-10= +6 X3a +X3a-X1a+X1b-X3b +6-7+5-3= +1 X3c +X3c-X3b+X1b-X1c +14-3+5-9= +7 Closed Path Since all improvement indices are all positive, optimal solution is obtained @ C= P 3,180. 10/13/13 16
  • 17. This method is an algorithm that finds an initial feasible solution to transportation problem by considering the “penalty cost” of not choosing cheapest available route. Opportunity Cost- the cost of opportunities that are sacrificed in order to take a specific action. 10/13/13 17
  • 18. 1. For each row with an available supply and each column with an unfilled demand, calculate an opportunity/penalty cost by subtracting the smallest cost per unit entry from the second smallest entry for a minimization problem. While, for maximization problem, opportunity cost is calculated by getting the difference between the highest and the second highest entry. 2. Identify the row or column with largest opportunity(difference) cost. 3. Allocate maximum amount possible to the available route with the lowest cost for minimization or highest revenue for maximization in the row/column selected from step 2. 4.Reduce appropriate supply and demand by the amount allocated in step 3. 5. Remove any rows with zero available supply and columns with unfilled demand for further consideration. 6.Return to step 1. 10/13/13 18
  • 19. From To A (CEU) 7 1 (STA. MESA) X1a 2 (TAFT AVE) 3 (DIVISORIA) Demand B (FEU) C (UE) 5 X1b 12 X2b 100 10 X3b 80 200 14 50 X2c 3 6 X3a 9 150 X1c 10 X2a Supply X3c 220 400 Original Transportation Tableau 10/13/13 19
  • 20. OPPORTUNITY COST FOR THE FIRST ALLOCATION Row/ Column Lowest Cost Opportunity Cost 1 7 5 2 2 10 10 0 3 6 3 3 A 7 6 1 B 5 3 2 C 10/13/13 Second Lowest Cost 10 9 1 20
  • 21. From To A (CEU) B (FEU) C (UE) Supply 7 5 9150 2 10 12 10 200 (TAFT AVE) 3 (DIVISORIA) 6 14 50 1 (STA. MESA) Demand 100 50 3 80 220 400 First allocation 10/13/13 21
  • 22. OPPORTUNITY COST FOR THE SECOND ALLOCATION Row/ Column Lowest Cost Opportunity Cost 1 7 5 2 2 10 10 0 A 10 7 1 B 12 5 7 C 10/13/13 Second Lowest Cost 10 9 1 22
  • 23. From To A (CEU) B (FEU) 7 1 (STA. MESA) Supply 5 9150 12 10 200 14 50 30 2 10 (TAFT AVE) 3 (DIVISORIA) 6 Demand C (UE) 100 50 3 80 220 400 Second allocation 10/13/13 23
  • 24. OPPORTUNITY COST FOR THE THIRD ALLOCATION Row/ Column Lowest Cost Opportunity Cost 1 9 7 2 2 10 10 0 A 10 7 1 C 10/13/13 Second Lowest Cost 10 9 1 24
  • 25. From To 1 (STA. MESA) A (CEU) B (FEU) 7 100 30 (TAFT AVE) 3 (DIVISORIA) 6 12 80 220 10 200 14 200 50 3 Third allocation 10/13/13 9150 20 10 100 Supply 5 2 Demand C (UE) 50 400 C= P 3180 25
  • 26. • DEGENERATE PROBLEMS >> (column + row) – 1 , SSM not applicable >> put zero (0) to any unused square and proceed with SSM steps • UNBALANCED PROBLEMS >> demand ≠ supply >> add dummy column/row 10/13/13 26
  • 27. Otsukaresamadeshita!!! Thank You for listening!!!  10/13/13 27

Hinweis der Redaktion

  1. Presented by: Cassandra Giselle Gaas Calvin Caduhada Jelailyn Villanueva Pamela Blanch Almendra Nesie Rose Apa-ap Jayson Arciete
  2. Universities have ordered desktop computers that must be delivered and installed by the beginning of the academic year:
  3. With cost minimization as criterion, Epsilon Company wants to determine how many desktop computers should be shipped from each warehouse to each university. Compare alternatives using, Northwest Corner Rule (NCR) Least Cost Method (LCM) Stepping Stone Method Vogel’s Approximation Method (VAM)
  4. Objective Function: C= cost of shipment of all Xij= no. of computerd delivered i= origin ; j= destination
  5. C= 700 + 250 + 360 + 1700 + 700 = P 3710
  6. C = P 3190
  7. Table derived through Northwest Corner Rule
  8. Improvement Index- the increase/decrease in total cost that would result from reallocating one unit to an unused square.
  9. Improved Transportation Tableau @ C= P 3180
  10. Since all improvement indices are all positive, optimal solution is obtained.
  11. Previously improved transportation tableau
  12. New improved Transportation Tableau
  13. Again, computing for improvement indices:
  14. Opportunity Cost- the cost of opportunities that are sacrificed in order to take a specific action.
  15. Steps: For each row with an available supply and each column with an unfilled demand, calculate an opportunity/penalty cost by subtracting the smallest cost per unit entry from the second smallest entry for a minimization problem. Identify the row or column with largest opportunity(difference) cost. Allocate maximum amount possible to the available route with the lowest cost for minimization or highest revenue for maximization in the row/column selected from step 1. Reduce appropriate supply and demand by the amount allocated in step 2. Remove any rows with zero available supply and columns with unfilled demand for further consideration Return to step 1.
  16. Original Transportation Tableau
  17. OPPORTUNITY COST FOR THE FIRST ALLOCATION
  18. First allocation
  19. C= P 3180
  20. DEGENERATE PROBLEMS UNBALANCED PROBLEMS