Multiobjective linear fractional programming model with equality and inequality constraints under pentagonal intuitionistic fuzzy environment

Multi-objective Linear Fractional optimization simultaneously addresses diverse goals using fractional programming techniques, offering a versatile decision-making framework adaptable to complex problems. The integration of Multiobjective Linear Fractional principles within an Intuitionistic Fuzzy E...

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Vydané v:Opsearch Ročník 62; číslo 4; s. 1991 - 2028
Hlavní autori: Yuvashri, P., Saraswathi, A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New Delhi Springer India 01.12.2025
Springer Nature B.V
Predmet:
Application Article
Business and Management
Careers
Constraints
Cotton
Decision making
Equality
Expected values
Fuzzy logic
Fuzzy sets
Inventory
Linear programming
Linear transformations
Management
Mathematical programming
Mathematics
Multiple objective analysis
Operations Research/Decision Theory
Optimization
Parameter uncertainty
Pareto optimum
Problem solving
Production planning
Ratios
Sensitivity analysis
Uncertainty
Accuracy function
Pentagonal intuitionistic fuzzy number
Crisp multi objective linear programming problem
Fuzzy optimal solution
Multi-objective linear programming problem
ISSN:0030-3887, 0975-0320
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Abstract Multi-objective Linear Fractional optimization simultaneously addresses diverse goals using fractional programming techniques, offering a versatile decision-making framework adaptable to complex problems. The integration of Multiobjective Linear Fractional principles within an Intuitionistic Fuzzy Environment reshapes decision-making, optimizing multiple objectives concurrently amid imprecise information. This innovative fusion of linear fractional programming and intuitive, fuzzy logic forms a flexible framework to efficiently address complex problems, accounting for uncertainties and diverse decision criteria, expanding the solution spectrum comprehensively. In this research paper, the multi-objective optimization issue is modelled in a fuzzy intuitionistic environment. The cost of objective function, decision making variable and coefficients are to be pentagonal intuitionistic fuzzy number (PIFN). Furthermore in this paper we take up a problem where the constraints are both equality and inequality and the crisp version is achieved using novel fractional to linear transformation with aid of the accuracy function defuzzified the constraints where the uncertain parameters are represented as intuitionistic pentagonal fuzzy numbers. We have constructed alternate approach a to resolve multi-objective optimization problems. The suggested approach entails thinking objectively when making decisions. For the simultaneous optimal evaluation of each objective. The transformation which converted the IFMOLFPP into crisp multiobjective linear programming problem(CMOLPP) by proposed strategy. Where the converted CMOLPP was solved by using lingprog to get optimal solution for CMOLPP. The numerical examples are provided to demonstrate the effectiveness of the suggested solution technique. To show the practical applicability, a case study of airline issue is also examined.
AbstractList Multi-objective Linear Fractional optimization simultaneously addresses diverse goals using fractional programming techniques, offering a versatile decision-making framework adaptable to complex problems. The integration of Multiobjective Linear Fractional principles within an Intuitionistic Fuzzy Environment reshapes decision-making, optimizing multiple objectives concurrently amid imprecise information. This innovative fusion of linear fractional programming and intuitive, fuzzy logic forms a flexible framework to efficiently address complex problems, accounting for uncertainties and diverse decision criteria, expanding the solution spectrum comprehensively. In this research paper, the multi-objective optimization issue is modelled in a fuzzy intuitionistic environment. The cost of objective function, decision making variable and coefficients are to be pentagonal intuitionistic fuzzy number (PIFN). Furthermore in this paper we take up a problem where the constraints are both equality and inequality and the crisp version is achieved using novel fractional to linear transformation with aid of the accuracy function defuzzified the constraints where the uncertain parameters are represented as intuitionistic pentagonal fuzzy numbers. We have constructed alternate approach a to resolve multi-objective optimization problems. The suggested approach entails thinking objectively when making decisions. For the simultaneous optimal evaluation of each objective. The transformation which converted the IFMOLFPP into crisp multiobjective linear programming problem(CMOLPP) by proposed strategy. Where the converted CMOLPP was solved by using lingprog to get optimal solution for CMOLPP. The numerical examples are provided to demonstrate the effectiveness of the suggested solution technique. To show the practical applicability, a case study of airline issue is also examined.
Author Yuvashri, P.
Saraswathi, A.
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  surname: Saraswathi
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Keywords Accuracy function
Pentagonal intuitionistic fuzzy number
Crisp multi objective linear programming problem
Fuzzy optimal solution
Multi-objective linear programming problem
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Snippet Multi-objective Linear Fractional optimization simultaneously addresses diverse goals using fractional programming techniques, offering a versatile...
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springer
SourceType Aggregation Database
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Publisher
StartPage 1991
SubjectTerms Application Article
Business and Management
Careers
Constraints
Cotton
Decision making
Equality
Expected values
Fuzzy logic
Fuzzy sets
Inventory
Linear programming
Linear transformations
Management
Mathematical programming
Mathematics
Multiple objective analysis
Operations Research/Decision Theory
Optimization
Parameter uncertainty
Pareto optimum
Problem solving
Production planning
Ratios
Sensitivity analysis
Uncertainty
Title Multiobjective linear fractional programming model with equality and inequality constraints under pentagonal intuitionistic fuzzy environment
URI https://link.springer.com/article/10.1007/s12597-024-00864-z
https://www.proquest.com/docview/3268924649
Volume 62
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