Optimizing multi-level decision-making: M-TOPSIS approach for quadratic fractional multiobjective problems

This paper discusses the significance of the multi-level multiobjective quadratic fractional programming problem (ML-MOQFPP) within hierarchical organizations and the challenges it faces in achieving success. It introduces the M-TOPSIS approach, which aims to address these challenges by utilizing me...

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Vydáno v:International journal of system assurance engineering and management Ročník 16; číslo 8; s. 2832 - 2840
Hlavní autoři: Rani, Rozy, Goyal, Vandana, Gupta, Deepak
Médium: Journal Article
Jazyk:angličtina
Vydáno: New Delhi Springer India 01.08.2025
Springer Nature B.V
Témata:
Algorithms
Conflict resolution
Decision making
Engineering
Engineering Economics
Fuzzy sets
Linear programming
Logistics
Marketing
Mathematical programming
Multiple objective analysis
Objectives
Optimization
Organization
Original Article
Preferences
Quadratic equations
Quality Control
Reliability
Safety and Risk
Set theory
Variables
Positive ideal solution (PIS)
Multiobjective
Negative ideal solution (NIS)
Membership function
Modified-TOPSIS approach (M-TOPSIS)
ML-MOQFPP
ISSN:0975-6809, 0976-4348
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Shrnutí:This paper discusses the significance of the multi-level multiobjective quadratic fractional programming problem (ML-MOQFPP) within hierarchical organizations and the challenges it faces in achieving success. It introduces the M-TOPSIS approach, which aims to address these challenges by utilizing membership functions and distances of the positive and negative ideal solutions at each level. The primary objective of M-TOPSIS is to maximize membership goals while minimizing deviational variables, thereby obtaining optimal outcomes for decision variables. It elaborates on the problem formulation involving multi-level multiobjective decision-making with quadratic constraints, emphasizing the minimization of fractional quadratic functions at each level through the M-TOPSIS algorithm. Additionally, it provides an illustrative numerical example and algorithm to validate the proposed methodology, concluding with comparative discussions against existing approaches.
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ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-025-02830-4