Online ISSN: 2515-8260

Keywords : Transportation problem


AN ALGORITHM FOR SOLVING HESITANT FUZZY QUADRATIC FRACTIONAL TRANSPORTATION PROBLEM

A. Saranya; I. Fracina Nishandhi; F.S. Josephine

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 3077-3084

Hesitant fuzzy set plays a vital role in group decision making problems when there are several possible memberships for an element to a set. This paper presents the algorithm to solve the hesitant fuzzy quadratic fractional transportation problem. The coefficients of the quadratic fractional transportation problem are considered as hesitant fuzzy elements. The proposed algorithm determines the optimal solution to the hesitant fuzzy quadratic fractional transportation problem. The numerical problem is solved to show the efficiency of the proposed approach.

AN ALGORITHM FOR MULTI OBJECTIVE FUZZY FRACTIONAL TRANSPORTATION PROBLEM

J.Merline Vinotha; W. Ritha; I. Antonitte Vinoline

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 3142-3153

In this paper a new algorithm is proposed to find the optimal solution of the multi objective fuzzy fractional transportation problem. The proposed algorithm is very simple and easy to understand. This algorithm gives the better solution in both crisp environment and fuzzy environment. The numerical example is solved to explain the algorithm. The solution of the problem is compared with the several existing problem. The proposed algorithm gives the better solution than the existing one.

An Approach for Solving FuzzyTransportation Problemusing Ranking function

P.Sagaya Leeli; S.Jone Jayashree; J. Beny

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 11, Pages 5863-5870

 In this paper we shall study fuzzy transportation problem, and we introduce an approach for solving a wide range of such problem by using a method which apply it for ranking of the fuzzy numbers. Some of the quantities in a fuzzy transportation problem may be fuzzy or crisp quantities In many fuzzy decision problems, the quantities are represented in terms of fuzzy numbers Fuzzy numbers may , triangular or trapezoidal or any LR fuzzy number. Thus,some fuzzy numbers are not directly comparable. First, we transform the fuzzy quantities as the cost, coefficients, supply and demands, in to crisp quantities by using our method and then by using the classical algorithms we solve and obtain the solution of the problem. The new method is a systematic procedure, easy to apply and can be utilized for all types of transportation problem whether maximize or minimize objective function.At the end, this method is illustrated with a numerical example