FUZZY AGGREGATION FOR RANKING IN BUSINESS

Today, businesses depend strongly on data and the opinion of customers or the experience of managers or experts. The large databases contain non-heterogeneous data, which is the ground for further decisions. Business uses multicriterial decisions in more areas (e.g., customer care, marketing, product development, risk management, HR, etc.) and often it is based on assessment. One of the assessment methods is the ranking, which can be done by crisp values of data where the sharp borders between evaluated entities do not give the adequate ranking result. On the other hand, the ranking process is based on the qualitative assessment, which has linguistic expression. It is more familiar and understandable for people. The article shows how to treat non-heterogeneous data to prepare them for a ranking process using fuzzy sets theory. The article aims at offering several types of ranking methods based on different inputs and preferences of the user and describes appropriate fuzzy aggregations for solving the ranking problem.

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Psychological Well-Being in Adolescence: An Approach from Fuzzy Sets Theory

PsycEXTRA Dataset ◽

10.1037/e534572010-001 ◽

2010 ◽

Author(s):

Monica Gonzalez ◽

Ferran Casas

Keyword(s):

Fuzzy Sets ◽

Well Being ◽

Fuzzy Sets Theory ◽

Psychological Well Being ◽

Sets Theory

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Condition Assessment Framework for Facility Management Based on Fuzzy Sets Theory

Buildings ◽

10.3390/buildings11040156 ◽

2021 ◽

Vol 11 (4) ◽

pp. 156

Author(s):

Deniz Besiktepe ◽

Mehmet E. Ozbek ◽

Rebecca A. Atadero

Keyword(s):

Fuzzy Sets ◽

Condition Index ◽

Condition Assessment ◽

Facility Management ◽

Maintenance Management ◽

Fuzzy Sets Theory ◽

Mean Time Between Failures ◽

Condition Rating ◽

Computerized Maintenance Management System ◽

Sets Theory

Condition information is essential to develop effective facility management (FM) strategies. Visual inspections and walk-through surveys are common practices of condition assessment (CA), generally resulting in qualitative and subjective outcomes such as “poor”, “good”, etc. Furthermore, limited resources of the FM process demand that CA practices be efficient. Given these, the purpose of this study is to develop a resource efficient quantitative CA framework that can be less subjective in establishing a condition rating. The condition variables of the study—mean time between failures, age-based obsolescence, facility condition index, occupant feedback, and preventive maintenance cycle—are identified through different sources, such as a computerized maintenance management system, expert opinions, occupants, and industry standards. These variables provide proxy measures for determining the condition of equipment with the implementation example for heating, ventilating, and air conditioning equipment. Fuzzy sets theory is utilized to obtain a quantitative condition rating while minimizing subjectivity, as fuzzy sets theory deals with imprecise, uncertain, and ambiguous judgments with membership relations. The proposed CA framework does not require additional resources, and the obtained condition rating value supports decision-making for building maintenance management and strategic planning in FM, with a comprehensive and less subjective understanding of condition.

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