This chapter aims basically to ensure two benefits, which focus on method and application. Firstly, this chapter proposes a new approach, which is TOPSIS (technique for order preference by similarity to ideal solution) based on interval type-2 trapezoidal fuzzy (IT2TrF) numbers. Secondly, the proposed method is applied to failure mode and effects analysis (FMEA) problem to overcome the drawbacks of traditional FMEA, which determines possible failure modes. In traditional FMEA, the risk priority number method, which is obtained as the product of occurrence, severity, and detection risk factors, has been debated to have several drawbacks. The implementation of risk assessment is realized to indicate the validity of the suggested approach in a construction building. The results of the proposed approach and some other methods are compared. The aim and resources are optimized by using linear programming.
TopIntroduction
An occupational accident is an event that concerns not only the employees, but also the family, the firm and the state. Those who are exposed to work accidents and occupational diseases are employees but also have costs for the company and society. The material and moral difficulties experienced by the person and his / her family who suffered a work accident, the companies that suffered serious financial losses due to work accidents, the state which lost both the healthy and productive work force and the ones who lost the material were the parties who suffered as a result of these accidents. Failure mode and effects analysis (FMEA) is a widely utilized risk assessment method to identify and eliminate potential defects in manufacturing and service systems. The failure mode is defined as the situations in which a potential failure may occur in the part, subsystem, system, or process when performing the design purpose (Wang et al., 2009).
The difference between FMEA and other risk assessment tools is that, instead of finding a solution after a failure occurs, problems are eliminated in connection with the system's predetermination. This helps decision makers to correct existing programs, to take action to reduce the likelihood of failure, and to avoid dangerous accidents. Today, FMEA is widely used in many fields of industry (Efe et al., 2017).
A system, design, process or service usually has many failure modes and effects. In this case, it is necessary to prioritize and evaluate each failure mode and effect according to the risk values. The high-risk (or most dangerous) failure mode should be corrected first. The traditional FMEA uses risk priority number (RPN) method to determine the risk priorities of failure modes. The conventional FMEA determines the risk priorities of the failure modes with RPN, which is the product of the probability (O), severity (S), and detectability (D) of a failure mode.
(1)This chapter consists of five parts. The second part handles literature review about FMEA. The used methods are presented in Part three. Part four shows an application to test the validity of the presented approach. The last part introduces the conclusion of the study.
TopLiterature Review
Different methods have been presented to cope with the drawbacks of the traditional FMEA. The related papers are introduced in Table 1.
Table 1. Methods | Authors |
Sezgisel bulanık küme- DEMATEL | Chang and Cheng (2010) |
Bulanık AHP- Bulanık TOPSIS | Kutlu and Ekmekçioğlu (2012) |
Bulanık AHP- Entropi-Bulanık VIKOR | Liu et al. (2015) |
Sezgisel bulanık küme- Öklid mesafe operatörü | Liu et al. (2014) |
Fuzzy evidential reasoning and belief rule-based approach | Liu et al. (2013) |
Fuzzy inference system | Jee et al. (2015) |
Fuzzy and grey theories | Zhou and Thai (2016) |
Hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach | Liu et al. (2016) |
Z numbers based AHP, entropy and VIKOR methods | Mohsen and Fereshteh (2017) |
Fuzzy PROMETHEE | Efe et al. (2016) |
Intuitionistic fuzzy AHP - VIKOR methods | Efe et al. (2017) |
Fuzzy best-worst, relative entropy, VIKOR | Tian et al. (2018) |
MULTIMOORA (Multi-objective Optimization By Ratio Analysis) and AHP | Fattahi and Khalilzadeh (2018) |
Quality function deployment and intuitionistic fuzzy VIKOR | Efe (2019b) |
Fuzzy ANP | Yazdani et al. (2019) |
Intuitionistic fuzzy best-worst method | Yazdi et al. (2020) |
Interval type-2 fuzzy evidential reasoning method | Qin et al. (2020) |
Regret theory and PROMETHEE under linguistic neutrosophic context | Zhu et al. (2020) |