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Moreover, they will also satisfy the inconsistence information under these conditions, i.e., represent the maximum degree of the membership (i.e., the degree of acceptance of quantity) and the non-membership (i.e., the degree of rejection of quantity), respectively.The cost values are also in Pythagorean fuzzy numbers where , it is clear that the Py FN transportation cost of IBFS is 41.45 which is same as the optimum transportation cost of Py FN transportation problem.However, these values are generally imprecise or vague. However, the fuzzy set takes only a membership function.
The following section includes the existing method under crisp and fuzzy transportation problems.
In the next following section, the proposed method for solving the transportation problem is discussed.
This complete scenario has motivated us to come up with a new method for solving TP with the Pythagorean fuzzy range which are formulated and solved with the use of the proposed algorithm for the first time.
This paper is organized as follows: in the next section, some basic knowledge, concepts on Pythagorean fuzzy set theory and arithmetic operation on Pythagorean Fuzzy Numbers (PFNs) are presented.
Hence, this shows that the optimal value is not more than the IBFS and in Example , the Py FN transportation cost of IBFS is 0.132978 which is the same as the optimum transportation cost of Py FN transportation problem.
Again we observe that the optimum value is not more than the IBFS.
This problem aims to maintain the supply from source to destination.
Traditionally, it has been generally assumed that transversal costs of supply/demand are expressed in terms of crisp numbers.
There are various methods in the field of PFS to solve multi-criteria decision-making problems such as: extension of TOPSIS [ charts some significant influences towards transportation problem (TP).
Based on the previous discussions on TP and currently available data, there are no existing methods which are available for TP under Pythagorean fuzzy environment.