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This paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.

This paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.

The proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.

Mamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

The purpose of this paper is to analyze previous models of the concept of ranking accuracy within the weighted aggregated sum product assessment (WASPAS) method and make necessary refinements.

This paper presents a correct combination of the weighted sum model (WSM) and weighted product model (WPM), which is usually performed on an ad hoc basis in the literature.

One of the reasons of rarely conducting ranking accuracy analysis might be that some of the reported equations in the literature are confusing, and hence, accurate partial derivatives cannot be calculated. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed.

A corrected WASPAS equation for optimal combination parameters is derived. Two examples are used to validate the formulations, and software implementation is provided. Because multiple attribute decision-making (MADM) has gained widespread attention from both the academia and industry, the findings of this paper help decision makers fully capitalize the concept of ranking accuracy and avoid possible confusions regarding the equations reported in the literature.

WASPAS is a relatively new MADM method and has enjoyed a visible position in the MADM literature. In addition to its simplicity, the WASPAS method utilizes the concept of ranking accuracy by combining the well-known WSM and WPM. This combination realized via an optimization criterion brings unique opportunities for decision makers such as evaluating confidence intervals for relative significance of alternatives and reducing estimated variance of ranking results. Despite its crucial importance, the combination of WSM and WPM is usually performed on an ad hoc basis in the literature. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed along with clarifying examples.

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

The main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices.

In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices.

The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort.

In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation.

The finding of this study would help property investors for establishing more effective property management strategies by taking different geographical location conditions into account.

From the government side, knowing future rises, falls and turning points of property prices in different locations can allow the government to monitor the property price changes and control the speculation activities that cause a dramatic change in the market.

There is no previous research paper focusing on neighborhood-based clusters and forecasting house sale price indices in Turkey. At this point, it is the first academic study.