The fuzzy transportation problem is a very popular, well-known optimization problem in the area of fuzzy set and system. In most of the cases, researchers use type 1 fuzzy set as the cost of the transportation problem. Type 1 fuzzy number is unable to handle the uncertainty due to the description of human perception. Interval type 2 fuzzy set is an extended version of type 1 fuzzy set which can handle this ambiguity. In this paper, the interval type 2 fuzzy set is used in a fuzzy transportation problem to represent the transportation cost, demand, and supply. We define this transportation problem as interval type 2 fuzzy transportation problems. The utility of this type of fuzzy set as costs in transportation problem and its application in different real-world scenarios are described in this paper. Here, we have modified the classical Vogel’s approximation method for solved this fuzzy transportation problem. To the best of our information, there exists no algorithm based on Vogel’s approximation method in the literature for fuzzy transportation problem with interval type 2 fuzzy set as transportation cost, demand, and supply. We have used two Numerical examples to describe the efficiency of the proposed algorithm.

在模糊集和系统领域，模糊运输问题是一个非常流行的，众所周知的优化问题。在大多数情况下，研究人员使用1型模糊集作为运输问题的成本。类型1模糊数由于描述了人类的感知而无法处理不确定性。间隔类型2模糊集是类型1模糊集的扩展版本，可以处理这种歧义。本文在模糊运输问题中使用区间类型2模糊集来表示运输成本，需求和供应。我们将此运输问题定义为区间类型2模糊运输问题。本文介绍了这种类型的模糊集作为运输问题中的成本的效用及其在不同实际场景中的应用。这里，我们已经修改了经典的Vogel逼近方法来解决此模糊运输问题。据我们所知，文献中还没有基于Vogel逼近法的区间运输类型为2的模糊运输问题为运输成本，需求和供应的模糊运输问题的算法。我们已经使用两个数值示例来描述所提出算法的效率。