Z-Cognitive Map: An Integrated Cognitive Maps and Z-Numbers Approach under Cognitive Information

Journal of System Management

دوره 6، شماره 2، آذر 2020، صفحه 81-102   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.30495/jsm.2020.677237

نویسندگان

Mostafa Izadi¹؛ Rassoul Noorossana^{* 2}؛ Hamidreza Izadbakhsh³؛ Saber Saati⁴؛ Mohammad Khalilzadeh⁵

¹Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

²Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

³Department of Industrial Engineering, Kharazmi University, Tehran, Iran

⁴Department of Mathematics, Tehran-North Branch, Islamic Azad University, Tehran, Iran

⁵Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

چکیده

Usually, in real-world engineering problems, there are different types of uncertainties about the studied variables, which can be due to the specific variables under investigation or interaction between them. Fuzzy cognitive maps, which addresses the cause-effect relation between variables, is one of the most common models for better understanding of the problems, especially when the quantitative data are not available or the nature of the problem is qualitative. One of the significant issues of automatic construction of fuzzy cognitive maps is that it does not consider the experts' uncertainties because of their cognitive attitudes such as age, experience, etc., which affects the quality and validity of modeling of the complex problems. Therefore, in this paper, a Z-Cognitive maps approach is proposed based on the distance-based automatic construction approach and Z-numbers to capture uncertainty and ambiguity of experts' opinions on the variables and causality relationships. The suggested approach can act as decision support for cognitive maps problems considering the experts' cognitive information, which is tested by a numerical example.

کلیدواژه‌ها

Z-Numbers؛ Fuzzy Cognitive Map؛ Cognitive Information؛ Uncertainty Management