Lattice Ordered N-Soft Sets and Their Applications in Decision Making

Decision-making processes heavily rely on the natural ordering structure that exists within attributes. This manuscript presents the new mathematical framework of lattice-ordered N-soft sets (LON-SS) and anti-lattice-ordered N-soft sets (A-LON-SS) that expands traditional N-soft sets (N-SSs) through attribute order relation integration. Our methodology extends the N-SS theory by resolving its limitations regarding ordered parameter handling, especially during rating and ranking situations. Further, this manuscript presents a thorough mathematical treatment and multiple examples that show how LON-SS effectively handles real-world decision problems that contain natural hierarchical parameter relationships. The examination supports that LON-SS delivers superior computational efficiency and user-friendly processing compared to traditional methods when applying the TOPSIS algorithm to decision analysis tasks. The LON-SS framework delivers substantial benefits to decision-making scenarios that need multi-valued assessments together with ordered attributes while showing potential applications in evaluation systems preference modeling and resource allocation problems. In the end, this manuscript compares the proposed concept with certain existing ones to reveal the supremacy and need of the existing theory.

Authors

Institutions

Publication Details

Journal
Journal of Intelligent & Fuzzy Systems
Published
2025-06-27
DOI
https://doi.org/10.1177/18758967251334234
Primary Topic
Fuzzy and Soft Set Theory
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lattice Ordered N-Soft Sets and Their Applications in Decision Making

Ubaid ur Rehman, Khizar Hayat, Jabbar Ahmmad, Tahir Mahmood
Journal of Intelligent & Fuzzy Systems
Fuzzy and Soft Set Theory
article

Lattice Ordered N-Soft Sets and Their Applications in Decision Making

Ubaid ur Rehman, Khizar Hayat, Jabbar Ahmmad, Tahir Mahmood
article en

Abstract

Decision-making processes heavily rely on the natural ordering structure that exists within attributes. This manuscript presents the new mathematical framework of lattice-ordered N-soft sets (LON-SS) and anti-lattice-ordered N-soft sets (A-LON-SS) that expands traditional N-soft sets (N-SSs) through attribute order relation integration. Our methodology extends the N-SS theory by resolving its limitations regarding ordered parameter handling, especially during rating and ranking situations. Further, this manuscript presents a thorough mathematical treatment and multiple examples that show how LON-SS effectively handles real-world decision problems that contain natural hierarchical parameter relationships. The examination supports that LON-SS delivers superior computational efficiency and user-friendly processing compared to traditional methods when applying the TOPSIS algorithm to decision analysis tasks. The LON-SS framework delivers substantial benefits to decision-making scenarios that need multi-valued assessments together with ordered attributes while showing potential applications in evaluation systems preference modeling and resource allocation problems. In the end, this manuscript compares the proposed concept with certain existing ones to reveal the supremacy and need of the existing theory.

Journal of Intelligent & Fuzzy SystemsVol. 50(1)
International Islamic University, Islamabad (PK), University of Azad Jammu and Kashmir (PK), University of Management and Technology (PK)
Pakistan Science Foundation, Higher Education Commission, Pakistan
Openalex Percentile: Top 82%
Fuzzy and Soft Set Theory
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.