Development a Quantitative Framework for Multilayer Fuzzy Cognitive Maps by combining "Self-Organizing Map" and "Graph Theory and Matrix Approach" (SOM-GTMA)

Document Type : Research Paper

Authors

1 Ph.D. Cadidate in Operation Research Management, Faculty of Economic Management and Administrative Sciences, Semnan University, Semnan, Iran.

2 Assistant Professor, Department of Operation Research, Faculty of Mathematics, Semnan University, Semnan, Iran.

3 Professor, Department of Industrial Management, Faculty of management, Tarbiat Modares University, Tehran, Iran.

4 Assistant Professor, Department of Industrial Management, Faculty of Management and Accunting, Shahid Beheshti University, Tehran, Iran.

Abstract

Objective: The purpose of this study is to develop and improve the multilayer fuzzy cognitive maps in structuring and analysis of problems with high dimensions by providing a quantitative framework.
Methods: In this study, the Self-Organizing Map method and Graph Theory and Matrix Approach has been combined in the multilayer fuzzy cognitive maps approach. Based on this approach, problem structuring is done by clustering and creating a multilayer structure for cognitive mapping.
Results: The developed method in the present study has been used to analyze the problem of sustainable supply chain management achievement in the petrochemical industry. According to the results of data analysis based on the presented approach, "cooperation in the supply chain", "organizational development" and "management commitment to sustainable development" are the most effective factors in enabling sustainable supply chain management.
Conclusion: Based on the method presented in the present study, the problem is modeled by clustering components and creating a multilayer structure for cognitive mapping. The method presented in the present study can model problems with a large number of intervening variables. The proposed method in this study can model problems with a high number of variables.

Keywords

References

 
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Industrial Management Journal
Volume 13, Issue 1
2021
Pages 80-104

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