Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment
The evaluation and selection of reliable suppliers are an extremely important operational activity for any supply chain management. It is a hard problem since supplier selection is typically a multi-criteria decision-making problem involving several conflicting qualitative and quantitative criteria...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2022.2079220 |
| _version_ | 1797718980185554944 |
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| author | Md. Mohibul Islam Masahiro Arakawa |
| author_facet | Md. Mohibul Islam Masahiro Arakawa |
| author_sort | Md. Mohibul Islam |
| collection | DOAJ |
| description | The evaluation and selection of reliable suppliers are an extremely important operational activity for any supply chain management. It is a hard problem since supplier selection is typically a multi-criteria decision-making problem involving several conflicting qualitative and quantitative criteria on which the decision-maker’s knowledge is usually vague and imprecise. The selection process may be biased while the decision is made by single expert instead of multiple experts. This paper shows an integrated fuzzy approach to solve this problem. In this study, the proposed multi-criteria group decision-making model is developed by integrating intuitionistic fuzzy set (IFS), fuzzy analytic hierarchy process (fuzzy AHP), and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS). The weight of the decision-maker is determined by using the intuitionistic fuzzy set, criteria weight is determined by the fuzzy AHP, and both weights are applied in the fuzzy TOPSIS method for ranking the suppliers. The efficiency and practicability of the presented approach are numerically demonstrated by solving a numerical example. Finally, sensitivity analysis is performed to test the robustness and stability of the introduced methodology. The result shows that the proposed model is more robust compared to other approaches. |
| first_indexed | 2024-03-12T08:58:06Z |
| format | Article |
| id | doaj.art-c5e53189822244f78b986c1f389c6dca |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T08:58:06Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-c5e53189822244f78b986c1f389c6dca2023-09-02T15:52:25ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2022.2079220Integrated multi-criteria group decision-making model for supplier selection in an uncertain environmentMd. Mohibul Islam0Masahiro Arakawa1Department of Architecture, Design, Civil Engineering and Industrial Management Engineering, Nagoya Institute of Technology, Nagoya, JapanDepartment of Architecture, Design, Civil Engineering and Industrial Management Engineering, Nagoya Institute of Technology, Nagoya, JapanThe evaluation and selection of reliable suppliers are an extremely important operational activity for any supply chain management. It is a hard problem since supplier selection is typically a multi-criteria decision-making problem involving several conflicting qualitative and quantitative criteria on which the decision-maker’s knowledge is usually vague and imprecise. The selection process may be biased while the decision is made by single expert instead of multiple experts. This paper shows an integrated fuzzy approach to solve this problem. In this study, the proposed multi-criteria group decision-making model is developed by integrating intuitionistic fuzzy set (IFS), fuzzy analytic hierarchy process (fuzzy AHP), and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS). The weight of the decision-maker is determined by using the intuitionistic fuzzy set, criteria weight is determined by the fuzzy AHP, and both weights are applied in the fuzzy TOPSIS method for ranking the suppliers. The efficiency and practicability of the presented approach are numerically demonstrated by solving a numerical example. Finally, sensitivity analysis is performed to test the robustness and stability of the introduced methodology. The result shows that the proposed model is more robust compared to other approaches.https://www.tandfonline.com/doi/10.1080/23311916.2022.2079220integrated modelsuppliers selectionmutually conflicting criteriauncertain environmentgroup decisionfuzzy logic |
| spellingShingle | Md. Mohibul Islam Masahiro Arakawa Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment Cogent Engineering integrated model suppliers selection mutually conflicting criteria uncertain environment group decision fuzzy logic |
| title | Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment |
| title_full | Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment |
| title_fullStr | Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment |
| title_full_unstemmed | Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment |
| title_short | Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment |
| title_sort | integrated multi criteria group decision making model for supplier selection in an uncertain environment |
| topic | integrated model suppliers selection mutually conflicting criteria uncertain environment group decision fuzzy logic |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2022.2079220 |
| work_keys_str_mv | AT mdmohibulislam integratedmulticriteriagroupdecisionmakingmodelforsupplierselectioninanuncertainenvironment AT masahiroarakawa integratedmulticriteriagroupdecisionmakingmodelforsupplierselectioninanuncertainenvironment |