Left Maximum Minimum Cost Ideal Process -Secure and Unhinged Grid in Haze Computing

: In this article, proposed method namely Maximum Minimum LeftAllotment method is applied to seek the feasible solution with respect to Cost Optimization from the basic feasible solution set for transportation problems. The proposed algorithm is a different way to obtain the feasible (or) may be optimal (for some extant) solution without anxiety of degeneracy condition.


Introduction
In transport departments and travelers are facing some problems to detect the cost of transportation.Now a days many proposals and procedures are developed in this connections in linear programming problems mainly reduce the cost [3].Particularly from warehouses and Godowns articles will be transported source place to designated places with cheap cost [1], [2].The cost of distribution from a source to a destination is directly comparative to the number of units shipped [1] [8], [9].
In Computer Science Engineering formally some techniques were used in internet connections and intranet connections with minimum expenditure.This achievement achieved by an experimental test with genuine ideas of applied Operation Research [5].
So many methods are available to find the minimum cost for the transportation in logistics and supply chain management [6], [7].Also the cost minimization for the medical management has been discussed widely along with different algorithms in effective manner [4].
Step 2: Choose the maximum element from givenPOM.
Step 3: Supply the maximum demand for the minimum component lies in the left side of the chosen maximum componentand delete the corresponding row (or) column.

Step 4:
Select the next maximum component from the remaining rows and columns in Newly Constructed Transportation Table (NCTT) and repeat the step 2 & 3 until degeneracy condition satisfied.

Note:
If the problem is notbalanced, make the problem as balanced by adding dummy zero row or dummy zero column in the given transportation table, then consider the allotment for the dummy zero row or dummy zero column in end iteration.
Example 1: Consider the following balanced pay off matrix to minimizethe cost.

Table 1
By applying the proposed algorithm, we get Step 1:The maximum cost in the following table no.1.1is 40 shaded to state the pivotelement.Allot the maximum possible demand of 8 units which lies in column 3, according to the minimum cost 1 in two various columns namely 2 and 3,by the procedure.

Table 1.1
Step 2:The next maximum cost in this following table no 1.2is 40 to be selected to state the pivot element.By the procedure 1the maximum possible demand 6 units must beallottedtogether with minimum cost 1 in column 2.

Table 1.2
Step 3:The next maximum cost in this following table no 1.3 is 40 to be certain to state the pivot element.By the procedure 1the maximum possible demand 22 units must be chosen composed with minimum cost 2 in column 1.

Table 1.3
Step 4:The next maximum cost in this following table no 1.4 is 40 to be particular to state the pivot element.By the procedure 1the maximum possible demand 7 units must be selectedtogether with minimum cost 2 in column 1.

Table 1.4
Step 5:The next maximum cost in this following table no 1.5 is 40 to be selected to state the pivot element.By the procedure 1the maximum likely demand 11 units must be allottedtogether with minimum cost 3 in column 1.

Table 1.5
Step 6: The next maximum cost in this following table no 1.6 is 40 to be selected to state the pivot element.By the procedure 1the maximum likely demand 9 units must be allottedtogether with minimum cost 8 in column 1.

Table 1.7
Supply the maximum possible demand 9 units in (1, 1) and 6units in (1, 2) which leads to the solution satisfying all the conditions.
Step 8:The resulting initial feasible solution is given below.Example 2: Consider the following balanced pay off matrix to minimize the cost.

Table 2
The resulting initial feasible solution is given below.

3.
Results and Discussion: Table 5 The proposed method gives14.74%more accuracy in optimal feasible solution than the existed Least Cost Optimization Method.