The main purpose of this study is to develop a way of solving multiattribute decision‐making problems (MADM), where the value of an attribute appears as picture hesitant fuzzy number and the attribute weight information is not entirely known. So as to acquire attribute weight vector, we build up a model of optimization that relies on the basic ideal of the conventional grey relational analysis method, by which attribute weight can be resolved. We build up another model of optimization for the uncommon circumstance where the attribute weight information is totally unknown. By solving this model, we get a basic and definite equation which can be utilized to be determined attribute weight. The grey relation degree between each alternative and positive ideal solution (PIS) and negative ideal solution (NIS) are determined. At the same time, a degree of relative relational is defined to decide all alternative order of ranking by ascertaining the grey relation degree to both the PIS and NIS. To examine the produced approach and to show its effectiveness and applicability an illustrative examples are given. A comparative analysis with pre‐existing MADM methods are considered to show the predominance of the proposed strategy.

The main purpose of this planned manuscript is to establish an algorithm for the solution of multiattribute decision-making (MADM) issues, where the experts utilizing linguistic variables provide the information about attributes in the form of picture hesitant fuzzy numbers. So, for the solution of these kinds of issues, we develop the TOPSIS algorithm under picture hesitant fuzzy environment using linguistic variables, which plays a vital role in practical applications, notably MADM issues, where the decision information is arranged by the decision-makers (DMs) in the form of picture hesitant fuzzy numbers. Finally, a sample example is given as an application and appropriateness of the planned method. At the end, we conduct comparison analysis of the planned method with picture fuzzy TOPSIS method and intuitionistic fuzzy TOPSIS method.

In this paper, we introduce the concept of (α, β)-bipolar fuzzy generalized bi-ideal in ordered semigroup, which is a generalization of bipolar-fuzzy generalized bi-ideal in ordered semigroup. Using this concept, we provide some characterization theorems. We prove that in regular ordered semigroup, the concept of (∈, ∈ ∨q)-bipolar fuzzy generalized bi-ideal and (∈, ∈ ∨q)-bipolar fuzzy bi-ideal coincide. We also introduce the upper/lower parts of (∈, ∈ ∨q)-bipolar fuzzy generalized bi-ideals and characterize the regular ordered semigroups in terms of lower part of (∈, ∈ ∨q)-bipolar fuzzy left (resp. right or two sided) ideals and (∈, ∈ ∨q)-bipolar fuzzy generalized bi-ideals.