Solving a multi-objective chance constrained hierarchical optimization problem under intuitionistic fuzzy environment with its application

Various optimization approaches have been developed and used for generating optimal solutions for different industry related optimization problems. The semantic representation of imprecise coefficients and various types of uncertainties arising in real life optimization problems are still a challeng...

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Veröffentlicht in:Expert systems with applications Jg. 217; S. 119595
Hauptverfasser: Sharma, Kirti, Singh, Vishnu Pratap, Ebrahimnejad, Ali, Chakraborty, Debjani
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.05.2023
Schlagworte:
Bi-level optimization problem
Chance constrained programming problem
Interval linear programming problem
Intuitionistic fuzzy numbers
TOPSIS method
IFN
TOPSIS method
DM
Interval linear programming problem
IFLPP
TIFN
Intuitionistic fuzzy numbers
Bi-level optimization problem
TOPSIS
Chance constrained programming problem
CCPP
ISSN:0957-4174, 1873-6793
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Zusammenfassung:Various optimization approaches have been developed and used for generating optimal solutions for different industry related optimization problems. The semantic representation of imprecise coefficients and various types of uncertainties arising in real life optimization problems are still a challenging task and require attention of academicians as well as professionals. Decision makers (DMs) often face problems in presenting the linguistically expressed constraints. The impreciseness of information also plays a key role in modelling and thus in decision making. The impreciseness of coefficients is usually handled by using various elements of fuzzy set theory. The uncertainty associated with constraints in optimization problem is handled by using stochastic programming approaches. These coefficients are usually revealed by a logical DM, who takes into account the scope of hesitation along with the imprecise nature of data. Such coefficients can be very well represented by using Intuitionistic fuzzy numbers. In this work, an approach for solving multi-objective bi-level chance constrained optimization problem in an intuitionistic fuzzy environment has been presented. The technological coefficients and the coefficients of objective functions are represented by Triangular Intuitionistic fuzzy numbers whereas normal random variables are used to represent the coefficient of resources. The concept of component wise optimization is used for the reduction of intuitionistic fuzzy objective into their equivalent deterministic form. The concept of alpha-beta cut, chance constrained programming and interval programming are then used to convert the problem into a deterministic multi-objective bi-level linear programming problem. The resultant deterministic optimization problem is then solved by using TOPSIS method. The production planning problem of a firm has been modelled and solved by using the proposed approach. •Multi-objective chance constrained hierarchical optimization problem is considered.•Technological coefficients are represented by Intuitionistic fuzzy numbers.•Normal random variables are used to represent coefficient of resources.•Fuzzy problem is first reduced to corresponding deterministic problem.•TOPSIS method and Fuzzy programming approach are used to solve the problem.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.119595