This study provides an innovative approach to neutrosophic algebraic structures by introducing a new structure called Neutrosophic Soft Cubic T-ideal (NSCTID), which combines T-ideal (TID) and neutrosophic Soft Cubic Sets (NSCSs) within the framework of PS-Algebra. Within the already-existing neutrosophic cubic structures, the addition of soft sets with the characteristics of TID makes this structure more desirable. The theoretical development of the proposed structure includes the application of fundamental ideas as union, intersection, the Cartesian product, and homomorphism. We also introduce (...) the notions of NSCTID-translation and NSCTID-multiplication to further enhance the structure of NSCTID. By applying the idea of translation and multiplication, we offer improved algorithm for neutrosophic cubic sets to deal with different parameters that are satisying the TID’s distinctive characteristics. Through this thorough research, we offer an elementary understanding of NSCTID and its capabilities, providing the way to new algebraic structures. (shrink)

The Fifth International Scientific Conference of the Faculty of Nursing, Port Said University 2019 Neutrosophic Information Systems and Nursing Scientific Research.

This paper is specialized with fuzzy random multi-objective unbalanced transportation data problems by using of fuzzy programming technique when the sources and destination parameters are fuzzy random variables in inequality type of constraints. In this paper, we focus on the solution procedure of the specified transportation data problems where the objective functions are minimization type and supplies and demands are replaced by the fuzzy random variables. We first convert the fuzzy random multi-objective unbalanced transportation problem into deterministic problem by using (...) fuzzy random chance constraints approach. By introducing the concept of linear membership function of fuzzy programming, multi-objective deterministic transportation problem is converted into single objective deterministic problem and then we solve it and we obtain the optimal compromise solution. Lastly a numerical example is provided for illustration the methodology. (shrink)