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# 63

TRANSPORTATION PROBLEMS

63.1 INTRODUCTION
A scooter production company produces scooters at the units
situated at various places (called origins) and supplies them to
the places where the depot (called destination) are situated.

## Here the availability as well as requirements of the various

depots are finite and constitute the limited resources.

## This type of problem is known as distribution or transportation

problem in which the key idea is to minimize the cost or the
time of transportation.

## In previous lessons we have considered a number of specific

linear programming problems. Transportation problems are also
linear programming problems and can be solved by simplex
method but because of practical significance the transportation
problems are of special interest and it is tedious to solve them
through simplex method. Is there any alternative method to solve
such problems?

63.2 OBJECTIVES
After completion of this lesson you will be able to:

## l solve the transportation problems by

(i) North-West corner rule;
(ii) Lowest cost entry method; and
(iii)Vogel’s approximation method.
l test the optimality of the solution.
112 :: Mathematics

## 63.3 MATHEMATICAL FORMULATION OF

TRANSPORTATION PROBLEM
Let there be three units, producing scooter, say, A 1 , A 2 and
A 3 from where the scooters are to be supplied to four depots say
B 1, B 2, B3 and B 4.

## Let the number of scooters produced at A1 , A 2 and A 3 be a1,

a2 and a3 respectively and the demands at the depots be b 2 , b 1,
b 3 and b 4 respectively.

## We assume the condition

a1+a2 +a3 = b 1 +b 2 +b 3 +b 4

## i.e., all scooters produced are supplied to the different depots.

Let the cost of transportation of one scooter from A1 to B 1 be
c 11. Similarly, the cost of transportations in other casus are also
shown in the figure and 63.1 Table 1.

## Let out of a1 scooters available at A 1, x 11 be taken at B 1

depot, x12 be taken at B 2 depot and to other depots as well, as
shown in the following figure and table 1.

a1 b1
x 11 (c 11 ) B1
A1 x1 2 ( c ) c 1)
12 x 21( 2
x
13 (c b2
13 ) x 22(c 22) B2
a2 x
A2 14 (c
14 ) x23 ( c ) b3
23
B3
)
( c 31 ) x
x 31 (2 c 32 24 (c
x3 ) 24 ) b4
a3
x 33( c 33 B4
A3
x 34 (c 34 )

Fig 63.1

## Total number of scooters to be transported form A 1 to all

destination, i.e., B1 , B2 , B 3, and B 4 must be equal to a1 .

## ∴ x11 +x 12+x 13+x 14 =a1 (1)

Transportation Problems :: 113

## Similarly, from A 2 and A 3 the scooters transported be equal

to a2 and a3 respectively.

## On the other hand it should be kept in mind that the total

number of scooters delivered to B 1 from all units must be equal
to b 1 , i.e.,

x11+x21+x 31 = b 1 (4)

## Similarly, x12+x22+x 32 = b 2 (5)

x13+x23+x 33 = b 3 (6)

x14+x24+x 34 = b 4 (7)

## With the help of the above information we can construct the

following table :
Table 1
Depot
Unit To B 1 To B 2 To B 3 To B 4 Stock

i,j
From A 1 x 11 (c 11 ) x 12 (c 12 ) x 13 (c 13 ) x 14 (c 14 ) a1

From A 2 x 21 (c 21 ) x 22 (c 22 ) x 23 (c 23 ) x 24 (c 24 ) a2

From A 3 x 31 (c 31 ) x 32 (c 32 ) x 33 (c 33 ) x 34 (c 34 ) a3

Requirement b1 b2 b3 b4

will be equal to

S = ∑
i,j
c ij x ij , (8)

## quantities c ij x ij must be summed over all i = 1,2,3 and all

j = 1,2,3,4.
114 :: Mathematics

## Thus we come across a linear programming problem given by

equations (1) to (7) and a linear function (8).

## We have to find the non-negative solutions of the system

such that it minimizes the function (8).

Note :
We can think about a transportation problem in a general
way if there are m sources (say A 1, A 2, ..., A m) and n destinations
(say B 1 , B2 ,....,B n). We can use ai to denote the quantity of goods
concentrated at points A i (i=1,2,...., m) and b j denote the quantity
of goods expected at points B j (j =1,2,...,n). We assume the
condition

demand for it.

## 63.3.1 Some Definitions

The following terms are to be defined with reference to the
transportation problems:

## (A) Feasible Solution (F.S.)

A set of non-negative allocations x ij ≥ 0 which satisfies the
row and column restrictions is known as feasible solution.

## (B) Basic Feasible Solution (B.F.S.)

A feasible solution to a m-origin and n-destination problem is
said to be basic feasible solution if the number of positive
allocations are (m+n–1).

## If the number of allocations in a basic feasible solutions are

less than (m+n–1), it is called degenerate basic feasible solution
(DBFS) (otherwise non-degenerate).

## (C) Optimal Solution

A feasible solution (not necessarily basic) is said to be optimal
if it minimizes the total transportation cost.
Transportation Problems :: 115

## 63.4 SOLUTION OF THE TRANSPORTATION

PROBLEM
Let us consider the numerical version of the problem stated
in the introduction and the mathematical formulation of the same
in the next section, as below in Table 2.

Table 2
Depot
Unit B1 B2 B3 B4 Stock

A1 c 11 =2 c 12 =3 c 13 =5 c 14 =1 a 1 =8

A2 c 21 =7 c 22 =3 c 23 =4 c 24 =6 a 2=10

A3 c 31 =4 c 32 =1 c 33 =7 c 34 =2 a 3=20

## (All terms are in hundreds)

∑ bai
j
In order to find the solution of this transportation problem
we have to follow the steps given below.

## (A) Initial Basic Feasible Solution :

There are three different methods to obtain the initial basic
feasible solution viz.

## In the light of above problem let us discuss one by one.

116 :: Mathematics

## (I) North-West corner rule

In this method we distribute the available units in rows and
column in such a way that the sum will remain the same. We
have to follow the steps given below.

(a) Start allocations from north-west corner, i.e., from (1,1) position.
Here min (a1, b1), i.e., min (8,6)=6 units. Therefore, the maximum
possible units that can be allocated to this position is 6, and
write it as 6(2) in the (1,1) position of the table. This completes
the allocation in the first column and cross the other positions,
i.e., (2,1) and (3,1) in the column. (see Table 3)
Table 3
Depot
Unit B1 B2 B3 B4 Stock

A1 6(2) 8–6=2

A2 × 10

A3 × 20

Requirement 6–6=0 8 9 15 32

(b) After completion of step (a), come across the position (1,2).
Here min (8–6,8)=2 units can be allocated to this position
and write it as 2(3). This completes the allocations in the
first row and cross the other positions, i.e., (1,3) and (1,4) in
this row (see Table 4).
Table 4
Depot
Unit B1 B2 B3 B4 Stock

## A1 6(2) 2(3) × × 2–2=0

A2 × 10

A3 × 20

Requirement 0 8–2=6 9 15 30
Transportation Problems :: 117

(c) Now come to second row, here the position (2,1) is already
been struck off, so consider the position (2,2). Here
min (10,8–2)=6 units can be allocated to this position and
write it as 6(3). This completes the allocations in second
column so strike off the position (3,2) (see Table 5)
Table 5
Depot
Unit B1 B2 B3 B4 Stock

A1 6(2) 2(3) × × 0

A2 × 6(3) 10–6=4

A3 × × 20

Requirement 0 0 9 15 24

(d) Again consider the position (2,3). Here, min (10–6,9)=4 units
can be allocated to this position and write it as 4(4). This
completes the allocations in second row so struck off the
position (2,4) (see Table 6).
Table 6
Depot
Unit B1 B2 B3 B4 Stock

A1 6(2) 2(3) × × 0

A2 × 6(3) 4(4) × 0

A3 × × 20

Requirement 0 0 9–4=5 15 20

(e) In the third row, positions (3,1) and (3,2) are already been
struck off so consider the position (3,3) and allocate it the
maximum possible units, i.e., min (20,9–4)=5 units and write
it as 5(7). Finally, allocate the remaining units to the position
(3,4), i.e., 15 units to this position and write it as 15(2).
118 :: Mathematics

## Keeping in mind all the allocations done in the above method

complete the table as follows:
Table 7
Depot
Unit B1 B2 B3 B4 Stock

A1 6(2) 2(3) × × 8

A2 × 6(3) 4(4) × 10

A3 × × 5(7) 15(2) 20

Requirement 6 8 9 15 38

## From the above table calculate the cost of transportation as

6×2 + 2×3 + 6×3 + 4×4 + 5×7 + 15×2
= 12 + 6 + 18 + 16 + 35 + 30
= 117
i.e., Rs. 11700.

## (II) Lowest cost entry method

(a) In this method we start with the lowest cost position. Here it
is (1,4) and (3,2) positions, allocate the maximum possible
units to these positions, i.e., 8 units to the position (1,4) and
8 units to position (3,2), write them as 8(1) and 8(1)
respectively, then strike off the other positions in row 1 and
also in column 2, since all the available units are distributed
to these positions.
Table 8
Depot
Unit B1 B2 B3 B4 Stock

A1 × × × 8(1) 0

A2 × 10

A3 8(1) 12

Requirement 6 0 9 7 22
Transportation Problems :: 119

(b) Consider the next higher cost positions, i.e., (1,1) and (3,4)
positions, but the position (1,1) is already been struck off so
we can’t allocate any units to this position. Now allocate the
maximum possible units to position (3,4), i.e., 7 units as
required by the place and write it as 7(2). Hence the
allocations in the column 4 is complete, so strike off the
(2,4) position.
Table 9
Depot
Unit B1 B2 B3 B4 Stock

A1 × × × 8(1) 0

A2 × × 10

A3 8(1) 7(2) 5

Requirement 6 0 9 0 15

(c) Again consider the next higher cost position, i.e., (1,2) and
(2,2) positions, but these positions are already been struck
off so we cannot allocate any units to these positions.

(d) Consider the next higher positions, i.e., (2,3) and (3,1) positions,
allocate the maximum possible units to these positions, i.e., 9
units to position (2,3) and 5 units to position (3,1), write them
as 9(4) and 5(4) respectively. In this way allocation in column
3 is complete so strike off the (3,3) position.

Table 10
Depot
Unit B1 B2 B3 B4 Stock

A1 × × × 8(1) 0

A2 × 9(4) × 1

## A3 5(4) 8(1) × 7(2) 0

Requirement 1 0 0 0 1
120 :: Mathematics

(e) Now only the position (2,1) remains and it automatically takes
the alloation 1 to complete the total in this row, therefore,
write it as 1(7).

With the help of the above facts complete the allocation table
as given below.
Table 11
Depot
Unit B1 B2 B3 B4 Stock

A1 × × × 8(1) 8

A2 1(7) × 9(4) × 10

## A3 5(4) 8(1) × 7(2) 20

Requirement 6 8 9 15 38

## 8×1 + 1×7 + 9×4 + 5×4 + 8×1 + 7×2

= 8 + 7 + 36 + 20 + 8 + 14

= 93

## (III) Vogel’s approximation method

(a1 ) Write the difference of minimum cost and next to
minimum cost against each row in the penalty column.
(This difference is known as penalty).

## (a2 ) Write the difference of minimum cost and next to

minimum cost against each column in the penalty row.
(This difference is known as penalty).
Transportation Problems :: 121

## We obtain the table as given below.

Table 12
Depot
Unit B1 B2 B3 B4 Stocks Penalties

## A3 (4) (1) (7) (2) 20 (1)

Requirement 6 8 9 15 38

## Penalties (2) (2) (1) (1)

(b) Identify the maximum penalties. In this case it is at column
one and at column two. Consider any of the two columns,
(here take first column) and allocate the maximum units to
the place where the cost is minimum (here the position (1,1)
has minimum cost so allocate the maximum possible units,
i.e., 6 units to this positon). Now write the remaining stock
in row one. After removing the first column and then by
repeating the step (a), we obtain as follows:
Table 13
Depot
Unit B2 B3 B4 Stocks Penalties

## A3 (1) (7) (2) 20 (1)

Requirement 8 9 15 32

## Penalties (2) (1) (1)

122 :: Mathematics

## (c) Identify the maximum penalties. In this case it is at row one

and at column two. Consider any of the two (let it be first
row) and allocate the maximum possible units to the place
where the cost is minimum (here the position (1,4) has
minimum cost so allocate the maximum possible units, i.e., 2
units to this position). Now write the remaining stock in
column four. After removing the first row and by repeating
the step(a), we obtain table 14 as given below.
Table 14
Depot
Unit B2 B3 B4 Stocks Penalties

## A3 (1) (7) (2) 20 (1)

Requirement 8 9 13 30

## Penalties (2) (3) (4)

(d) Identify the maximum penalties. In this case it is at column
four. Now allocate the maximum possible units to the minimum
cost position (here it is at (3,4) position and allocate maximum
possible units, i.e., 13 to this positon). Now write the
remaining stock in row three. After removing the fourth column
and then by repeating the step (a) we obtain table 15 as
given below.
Table 15
Depot
Unit B2 B3 Stock Penalties

Requirement 8 9

## Penalties (2) (3)

Transportation Problems :: 123

## (e) Identify the maximum penalties. In this case it is at row

three. Now allocate the maximum possible units to the
minimum cost position (here it is at (3,2) position and allocate
maximum possible units, i.e., 7 to this position). Now in order
to complete the sum, (2,2) position will take 1 unit and (2,3)
position will be allocated 9 units.

This completes the allocation and with the help of the above
informations draw table 16 as under.
Table 16
Depot
Unit B1 B2 B3 B4 Stock

A1 6(2) 2(1) 8

A2 1(3) 9(4) 10

A3 7(1) 13(2) 20

Requirement 6 8 9 15 38

## From the above facts calculate the cost of transportation as

6×2 + 2×1 + 1×3 + 9×4 + 7×1 + 13×2
= 12 + 2 + 3 + 36 + 7 + 26
= 86
i.e., Rs. 8600.
Note:
After calculating the cost of transportation by the above three
methods, one thing is clear that Vogel’s approximation method
gives an initial basic feasible solution which is much closer to
the optimal solution than the other two methods. It is always
worth while to spend some time finding a “good” initial solution
because it can considerably reduce the total number of iterations
required to reach an optimal solution.
124 :: Mathematics

## INTEXT QUESTIONS 63.1

1. Determine an initial basic feasible solution to the following
transportaion problem using north-west corner rule, where
Oi and D j represent ith origin and jth destination respectively.
Table 17
D1 D2 D3 D4 Supply

O1 10 8 5 9 14

O2 12 13 6 11 16

O3 8 7 10 6 5

Demand 6 10 15 4 35

## 2. Find the initial basic feasible solution to the following

transportation problem by lowest cost entry method.
Table 18
D1 D2 D3 D4 Supply

O1 3 7 5 5 38

O2 5 5 3 4 19

O3 2 4 4 5 16

O4 4 9 4 6 23

Demand 25 29 21 21 96
3. Determine an initial basic feasible solution using Vogel’s
method by considering the following transportation problem.
Table 19
D1 D2 D3 D4 Supply

O1 21 16 15 3 11

O2 17 18 14 23 13

O3 32 27 18 41 19

Demand 6 10 12 15 43
Transportation Problems :: 125

## (B) Test for Optimization :

In part (A) of this section we have learnt how to obtain an
initial basic feasible solution. Solutions so obtained may be
optimal or may not be optimal, so it becomes essential for us to
test for optimization.

solution.

## Definition: A basic feasible solution of an (m × n) transportation

problem is said to be non-degenerate if it has following
two properties :
(a) Initial basic feasible solution must contain exactly m+n–1
number of individual allocations.

## Independent positions of a set of allocations means that it is

always impossible to form any closed loop through these
allocations. See fig. given below.

Closed loop

## Non - independent positions

Independent positons
126 :: Mathematics

## Theorem: If we have a B.F.S. consisting of m+n–1 independent

positive allocations and a set of arbitrary number u i
and vj (i=1,2,...m; j=1,2,...n) such that crs = u r+v s for
all occupied cells ( r,s ) then the evaluation d ij
corresponding to each empty cell (i, j) is given by
d ij = c ij – (u i+v j)

## Algorithm for optimality test :

In order to test for optimality we should follow the procedure
as given below:

independent positions.
Step 2: Determine a set of m+n numbers
u i (i=1,2,....m) and vj (j=1,2,...n)
such that for each occupied cells (r,s)
c rs = u r+v s
Step 3: Calculate cell evaluations (unit cost difference)
d j for each empty cell (i,j) by using the formula
d ij = c ij – ( u i +vj )
Step 4: Examine the matrix of cell evaluation d ij for negative
entries and conclude that
(i) If all d ij > 0 ⇒ Solution is optimal and unique.
(ii)If all d ij ≥ 0 ⇒ At least one d ij = 0
⇒ Solution is optimal and alternate
solution also exists.
(iii) If at least one d ij < 0 ⇒ Solution is not optimal.
If it is so, further improvement is required by repeating
the above process. See step 5 and onwards.
Step 5: (i) See the most negative cell in the matrix [ dij ].
(ii) Allocate θ to this empty cell in the final allocation
table. Subtract and add the amount of this allocation
to other corners of the loop in order to restore feasibility.
(iii) The value of θ, in general is obtained by equating
to zero the minimum of the allocations containing –θ
Transportation Problems :: 127

## (not + θ) only at the corners of the closed loop.

(iv)Substitute the value of θ and find a fresh allocation
table.
Step 6: Again, apply the above test for optimality till you find
all d ij ≥ 0

## Computational demonstration for optimality test

Consider the initial basic feasible solution as obtained by
[Table (16)].

## Step 1: (i) In this table number of allocations = 3+4–1=6.

(ii) All the positions of allocations are independent.
Step 2: Determine a set of (m+n), i.e., (3+4) numbers u 1, u 2, u 3,
and v 1, v 2, v3 , and v4 . for each occupied cells.
For this consider the row or column in which the
allocations are maximum (here, let us take first row).
Now, take u 1 as an arbitrary constant (say zero) then
by using c ij = u i+v j try to find all u i and v j as
Table 20
B1 B2 B3 B4 ui

A1 2 1 0

A2 3 4 3

A3 1 2 1

vj 2 0 1 1

c 11 = 2 = u1+v1 = 0+v1 ⇒ v 1 = 2
then c 14 = 1 = u1+v4 = 0+v4 ⇒ v 4 = 1
then c 34 = 2 = u3 +v4 = u 3 +1 ⇒ u 3 = 1
then c 32 = 1 = u3+v2 = 1+v2 ⇒ v 2 = 0
then c 22 = 3 = u2 +v2 = u 2 +0 ⇒ u 2 = 3
then c 23 = 4 = u2+v3 = 3+v3 ⇒ v 3 = 1
Thus u 1 =0, u 2=3, u 3=1 and v 1 =2, v2 =0, v 3=1 and v 4=1.
128 :: Mathematics

## Step 3: Cost matrix for the empty positons

i.e., [ c ij ] = • 3 5 •

7 • • 6

4 • 7 •

## Matrix [ u i + v j ] for empty positions

• 0 1 •

i.e., [ ui + vj ] = 5 • • 4

3 • 2 •

• 3 4 •

1 • 5 •

## Step 4: Here all d ij > 0 ⇒ Solution obtained by Vogel’s

approximation method is an
optimal solution
Example A: For the transportation problem

Table 21

Warehouse →
Factory
Factory W1 W2 W3 W4 Capacity

F1 19 30 50 10 7

F2 70 30 40 60 9

F3 40 8 70 20 18

Warehouse 5 8 7 14 34
Requirement
Transportation Problems :: 129

## the initial basic feasible solution obtained by Vogel’s

approximation method is given below.
Table 22
W1 W2 W3 W4 Available

F1 5(19) 2(10) 7

F2 7(40) 2(60) 9

F3 8(8) 10(20) 18

Requirement 5 8 7 14

## Test this for optimality.

Solution :
Step 1: Number of allocations = 3+4–1=6 and they are in
independent positon.
Step 2: ui

(19) (10) 10

(40) (60) 60

(8) (20) 20

vj 9 –12 –20 0

Step 3: 30 50

[c ij] = 70 30

40 70

ui

–2 –10 10

[u i+v j ]= 69 48 60

29 0 20

vj 9 –12 –20 0
130 :: Mathematics

32 60

11 70

## Step 4: Since one d ij < 0, therefore, the solution is not optimal.

See step 5 and onwards for to find new allocations and
test of optimality

## Step 5: (ii) Available

5(19) 2(10) 7

+θ 7(40) 2(60) 9
–θ

8(8) 10(20) 18
–θ +θ

Required 5 8 7 14

(iii) min [ 8 – θ, 2 – θ ] = 0
i.e., 2 – θ = 0
i.e., θ = 2
Available
5(19) 2(10) 7
∴ 2(30) 7(40) 9
6(8) 12(20) 18
Required 5 8 7 14
This improved basic feasible solution gives the cost for
this solution as
5(19)+2(10)+2(30)+7(40)+6(8)+12(20) = Rs.743.

## Step 6: Test this improved solution for optimality by repeating

steps 1,2,3 and 4. In each step, following matrices are
obtained:
Transportation Problems :: 131

## Matrix [c ij] for empty cells Matrix for u i and v j

ui
• (30) (50) • •(19) •(10) –10

## (40) • (70) • •(8) •(20) 0

vj 29 8 18 20

Matrix [u i+v j ] for empty cells Matrix for d ij =[c ij]-[u i+v j ]
for empty cells

• –2 8 • • 32 42 •

51 • • 42 19 • • 18

29 • 18 • 11 • 52 •

## Since all d ij > 0, therefore, the solution given as improved

basic feasible solution is an optimal solution with minimum cost
= Rs.743.

## 1. Test for optimality of the solution obtained in question

1 of Intext Questions 63.1. Give the final allocation table
and the minimum cost for the problem.

## 2. Test for optimality of the solution obtained in question

2 of Intext Questions 63.1. Give the final allocation table
and the minimum cost for the problem.

## 3. Test for optimality of the solution obtained in question

3 of Intext Questions 63.1. Give the final allocation table
and the minimum cost for the problem.
132 :: Mathematics

## WHAT YOU HAVE LEARNT

START

Construct the cost matrix

Determine I.B.F.S. by any method
(i) North-West Corner rule.
(ii) Lowest cost entry method.
(iii) Vogel’s approximation method.

Is
No B.F.S. Yes
< non-degenerate >
?

Compute cell evaluation
d ij = [c ij]–[u i+vj] <
Choose the Select a maximum
largest value of θ so that
∨ negative cell one of the basic
No Is Yes evaluation for cell becomes
there any
d ij < 0
> indicating the empty maintain-
cell entering ing feasibility
the solution of new solution.
(Basic) ∨

Identify a loop which starts and
∨ ends at this cell connecting some
BFS or all basic cell making ± θ
is optimal adjustments in the cell at the
corners of this loop maintaining
feasibility.
<

STOP
Transportation Problems :: 133

TERMINAL QUESTIONS
1. Solve the following transportation problem :

B1 B2 B3 B4 Stock

A1 1 2 1 4 30

A2 3 3 2 1 50

A3 4 2 5 9 20

Requirement 20 40 30 10 100

## 2. Solve the following transportation problem :

B1 B2 B3 B4 Stock

A1 21 16 25 13 11

A2 17 18 14 23 13

A3 32 17 18 41 19

Requirement 6 10 12 15 43

## Also test it for optimality.

63.1
1.

D1 D2 D3 D4 Supply

O1 6(10) 8(8) 14

O2 2(13) 14(6) 16

O3 1(10) 4(6) 5

Demand 6 10 15 4 35
134 :: Mathematics

Cost = Rs.268
2.

D1 D2 D3 D4 Supply

## O1 9(3) 8(7) 21(5) 38

O2 19(3) 19

O3 16(2) 16

O4 21(9) 2(4) 23

Demand 25 29 21 21 96

Cost = Rs.474
3.
D1 D2 D3 D4 Supply

O1 11(3) 11

## O2 6(17) 3(18) 4(23) 13

O3 7(27) 12(18) 19

Demand 6 10 12 15 43

Cost = Rs.686

63.2
1. Find allocation table is
D1 D2 D3 D4 Supply

O1 4(10) 10(8) 14

O2 1(12) 15(6) 16

O3 1(8) 4(6) 5

Demand 6 10 15 4 35
Transportation Problems :: 135

## Minimum cost = (4×10)+(10×8)+(1×12)+(15×6)+(1×8)+(4×6)

= 40+80+12+90+8+24

= Rs.254

## 2. Final allocation table

D1 D2 D3 D4 Supply

O1 23(3) 15(5) 38

O2 13(5) 6(4) 19

O3 16(4) 16

O4 2(4) 21(4) 23

Demand 25 29 21 21 96

## minimum cost = (23×3)+(15×5)+(13×5)+(6×4)+(16×4)+(2×4)+(21×4)

= Rs.389
Alternate solution also exists.

a good exercise)

## 3. Final allocation table is

D1 D2 D3 D4 Supply

O1 11(3) 11

## O2 6(17) 3(18) 4(23) 13

O3 7(27) 12(18) 19

Demand 6 10 12 15 43
136 :: Mathematics

## minimum cost = (11×3)+(6×17)+(3×18)+(4×23)+(7×27)+(12×18)

= 33+102+54+92+189+216
= Rs.686
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