Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making Problems
Many applicable problems have multi-goals that optimize simultaneously, and decision-makers set imprecise aspiration levels for each goal. Although such types of problems solved by fuzzy optimization are common in the literature, intuitionistic fuzzy optimization techniques are more efficient to ha...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hawassa University
2025-04-01
|
| Series: | East African Journal of Biophysical and Computational Sciences |
| Subjects: | |
| Online Access: | https://www.aJol.info/index.php/eajbcs/article/view/294435 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850173002573938688 |
|---|---|
| author | Demmelash Mollalign Moges Berhanu Guta Wordofa Allen Rangia Mushi |
| author_facet | Demmelash Mollalign Moges Berhanu Guta Wordofa Allen Rangia Mushi |
| author_sort | Demmelash Mollalign Moges |
| collection | DOAJ |
| description |
Many applicable problems have multi-goals that optimize simultaneously, and decision-makers set imprecise aspiration levels for each goal. Although such types of problems solved by fuzzy optimization are common in the literature, intuitionistic fuzzy optimization techniques are more efficient to handle than fuzzy and classical optimization. This research study focused on establishing a novel method by combining the penalty function method with an interactive goal programming methodology for addressing multi-objective decision-making problems in an intuitionistic fuzzy environment. One of the challenge that exists in the literature of the optimization method under an imprecise decision environment is that it is not guaranteed to generate a Pareto-optimal solution for the introduced problem. Therefore, in order to ensure the Pareto-optimality of the obtained solution, the suggested method has developed a new aggregation operator, an appropriate relaxation of the constraint set, and a well-structured extended Yager membership function.
In addition, unlike other methods in the literature, the suggested method gives decision-makers the option to penalize the most unsatisfied objective function at a specific attained solution instead of starting from scratch and working their way through the problem. To illustrate the proposed method, we used a numerical example.
|
| format | Article |
| id | doaj-art-a15ebb92cce64eda91aa53e26edb3083 |
| institution | OA Journals |
| issn | 2789-360X 2789-3618 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Hawassa University |
| record_format | Article |
| series | East African Journal of Biophysical and Computational Sciences |
| spelling | doaj-art-a15ebb92cce64eda91aa53e26edb30832025-08-20T02:19:57ZengHawassa UniversityEast African Journal of Biophysical and Computational Sciences2789-360X2789-36182025-04-0152Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making ProblemsDemmelash Mollalign Moges0Berhanu Guta Wordofa1Allen Rangia Mushi2Department of Mathematics, Hawassa University, Hawassa, EthiopiaDepartment of mathematics, Addis Ababa University, Addis Ababa, EthiopiaDepartment of mathematics, University of Dares Salaam, Dares Salaam, Tanzania Many applicable problems have multi-goals that optimize simultaneously, and decision-makers set imprecise aspiration levels for each goal. Although such types of problems solved by fuzzy optimization are common in the literature, intuitionistic fuzzy optimization techniques are more efficient to handle than fuzzy and classical optimization. This research study focused on establishing a novel method by combining the penalty function method with an interactive goal programming methodology for addressing multi-objective decision-making problems in an intuitionistic fuzzy environment. One of the challenge that exists in the literature of the optimization method under an imprecise decision environment is that it is not guaranteed to generate a Pareto-optimal solution for the introduced problem. Therefore, in order to ensure the Pareto-optimality of the obtained solution, the suggested method has developed a new aggregation operator, an appropriate relaxation of the constraint set, and a well-structured extended Yager membership function. In addition, unlike other methods in the literature, the suggested method gives decision-makers the option to penalize the most unsatisfied objective function at a specific attained solution instead of starting from scratch and working their way through the problem. To illustrate the proposed method, we used a numerical example. https://www.aJol.info/index.php/eajbcs/article/view/294435Multiobjective decision-making; Intuitionistic fuzzy decision set; Goal programming; Interactive penalty function. |
| spellingShingle | Demmelash Mollalign Moges Berhanu Guta Wordofa Allen Rangia Mushi Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making Problems East African Journal of Biophysical and Computational Sciences Multiobjective decision-making; Intuitionistic fuzzy decision set; Goal programming; Interactive penalty function. |
| title | Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making Problems |
| title_full | Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making Problems |
| title_fullStr | Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making Problems |
| title_full_unstemmed | Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making Problems |
| title_short | Penalized Intuitionistic Fuzzy Goal Programming Method for Solving Multi-Objective Decision-Making Problems |
| title_sort | penalized intuitionistic fuzzy goal programming method for solving multi objective decision making problems |
| topic | Multiobjective decision-making; Intuitionistic fuzzy decision set; Goal programming; Interactive penalty function. |
| url | https://www.aJol.info/index.php/eajbcs/article/view/294435 |
| work_keys_str_mv | AT demmelashmollalignmoges penalizedintuitionisticfuzzygoalprogrammingmethodforsolvingmultiobjectivedecisionmakingproblems AT berhanugutawordofa penalizedintuitionisticfuzzygoalprogrammingmethodforsolvingmultiobjectivedecisionmakingproblems AT allenrangiamushi penalizedintuitionisticfuzzygoalprogrammingmethodforsolvingmultiobjectivedecisionmakingproblems |