Fuzzy Logic Approach in Risk Assessment

Fuzzy Logic Approach in Risk Assessment

Çetin Karahan (Directorate General of Civil Aviation, Turkey), Esra Ayça Güzeldereli (Afyon Kocatepe University, Turkey) and Aslıhan Tüfekci (Gazi University, Turkey)
Copyright: © 2018 |Pages: 17
DOI: 10.4018/978-1-5225-2255-3.ch588
OnDemand PDF Download:
List Price: $37.50


Risk is the likelihood of occurrence of any event that may obstruct the ability of organizations to achieve their strategic, financial and operational goals. It is of profound importance for the business management to detect risks and determine appropriate actions against in time. Risk assessment is a continuous and recursive process aimed at maximization of the use of opportunities while minimizing threats. There is a tendency in the field of risk assessment to prefer more quantitative methods to reduce unclarity. One such method is fuzzy logic. This study investigates fuzzy logic as an alternative to the classical methods that have been used for the purposes of risk assessment, which plays a crucial role in business action plans. Due to its similarity to the process of human reasoning and its success in cases of unclarity, fuzzy logic offers a number of advantages in this regard.
Chapter Preview


The concept of fuzzy logic was first introduced in 1965 by Prof. Lotfi A. Zadeh who developed Lukasiewicz’s multivalued logic to set theory and created what he called fuzzy sets – sets whose elements belong to it in different degrees. At the start, fuzzy logic was a theoretical concept with little practical application. In the 1970’s, Prof. Edrahim Mamdani of Queen Mary College, London, built the first fuzzy system, a steam-engine controller, and he later designed the first fuzzy traffic lights. His work led to an extensive development of fuzzy control applications and products (Cirstea, Dinu, McCormick & Khor, 2002, pp. 113-114).

Bellman and Zadeh (1970) developed an initial general theory on decision making in fuzzy environment which include three basic concepts as fuzzy goals, fuzzy constraints and fuzzy decisions. It is concluded that the proposed theory is generally has advantages according to the traditional probability theory.

Tah and Carr (1999) claimed that the current risk management techniques mostly based on the operational research techniques developed in 1960s and usually had failed to meet the needs of project managers. They introduced a fuzzy risk analysis model for a construction project to eliminate the past studies’ concentration on particular risks and proposed a model which have a generic and generally practicable representation.

The development of fuzzy set theory to fuzzy technology during the first half of the 1990s has been very fast. More than 16,000 publications have appeared since 1965. Most of them have advanced the theory in many areas. Quite a number of these publications describe, however, applications of fuzzy set theory to existing methodology or to real problems. In addition, the transition from fuzzy set theory to fuzzy technology has been achieved by providing numerous software and hardware tools that considerably improve the design of fuzzy systems and make them more applicable in practice (Zimmerman, 2001, p. xxi).

Hajiha, Roodposhti and Askary (2009) provided a risk assessment approach conducted on the basis of fuzzy logic for audit risk, inherent risk and control risk. The results are compared to a real case and the accuracy level of the results is discovered to be relatively higher.

Keropyan and Gil-Lafuente (2011) place the emphasis on the importance of the ability of making right decisions and provide examples of use of fuzzy logic in selection of the decision-making styles within the scope of strategic management.

Key Terms in this Chapter

Fuzzy Logic: A field of study which centers on the human reasoning process and operates by converting it into mathematical functions.

Risk: The possibility of any event occurring that may stop businesses or companies from achieving their strategic, financial and operational goals.

Fuzzy Inference System (FIS): The system which is a way of mapping an input space to an output space by using fuzzy set theory. FIS uses a collection of fuzzy membership functions and rules to generate an output.

Risk Assessment: The process of identifying, evaluating and managing potential events and situations to provide a reasonable level of confidence for the achievement of the business goals.

Complete Chapter List

Search this Book: