A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications
Introduction
Multiple criteria decision-making (MCDM) methods are frequently used to solve real world problems with multiple, conflicting, and incommensurate criteria and/or objectives. Hwang and Yoon (1981) have classified the MCDM methods into two categories: multi-attribute decision-making (MADM) and multi-objective decision-making (MODM). MADM techniques, unlike MODM, heavily involves human participation and judgments. Research on human judgments and decision making shows that the human brain is able to consider only a limited amount of information at any one time (Simpson, 1996), which makes it unreliable to take decisions when facing complex problems. Analytic Hierarchy Process (AHP), initially introduced by Saaty (1980), is by now one of the most widely applied MADM techniques, whose main strength lies in its impartial and logical grading system (reducing personal biases and allowing for comparing dissimilar alternatives), but also in its flexibility to be integrated with various techniques like Linear Programming, Quality Function Deployment, Fuzzy Logic, etc. (Saaty, Vargas, 2001, Vaidya, Kumar, 2006). This enables users to extract benefits from all the combined methods and achieve the desired goal in a better way.
As a practical popular methodology for dealing with fuzziness and uncertainty, the Fuzzy Logic combined with AHP, more commonly known as Fuzzy AHP or FAHP (van Laarhoven & Pedrycz, 1983), has found huge applications in recent years. According to a recent survey on Fuzzy MCDM techniques (Mardani, Jusoh, & Zavadskas, 2015), FAHP is the second most widely used technique in a stand-alone mode (just after AHP). Since, as of this writing, no state-of-the-art survey of FAHP has been issued – the latest survey being dedicated to AHP and dating back to 2006 (Vaidya & Kumar, 2006) – this article looks into the research papers with a view to understand the spread of FAHP in different fields. Based on a classification scheme, a reference repository has been established, including around 200 international journal papers (starting from 2004). Papers have been classified based on various dimension s, including the year of publication, application area, identified theme, authors’ nationality, etc. This survey goes along with an online and free testbed1 that makes the results and findings of this study available, as well as one or more fuzzy pairwise comparison matrices (FPCMs) from all reviewed papers. For the anecdote, the genesis of this state-of-the-art survey started from a personal motivation around the study and development of new consistency indexes for FPCMs’ weight derivation, which is still a sensitive issue in the FAHP literature. In this context, we started the collection of FPCMs from scientific articles, which has led to the achievement of this survey. We believe that this online testbed will serve as a ready reference for those who wish to apply, modify or extend FAHP in various application domains, including scientists (e.g., for benchmarking purposes), reviewers and journal editors (e.g., to evaluate the relevance of future research papers), and other categories of practitioners.
The proposed state-of the-art survey is structured as follows: Section 2 discusses to what extent MADM techniques are important to overcome complex real-life problems involving different categories of stakeholders, in various sectors. Section 2 also discusses in greater detail how FAHP stands with regard to existing MCDM techniques, and what are the basic principles underlying (F)AHP. Section 3 details the research methodology considered to collect, classify and analyze the journal papers. Section 4 provides the breakdown of the literature review. Section 5 offers a comprehensive summary of the survey outcomes, graphical representations, and concluding remarks. Section 6 presents the online FAHP/FPCMs testbed; conclusion and research directions follow.
Section snippets
Background
Section 2.1 discusses why MADM techniques are widely applied to various sectors today, along with a general overview of how FAHP stands with regard to existing MCDM techniques. Following this introduction, the theories/principles underlying AHP and FAHP are respectively presented in Sections 2.2 and 2.3.
Research methodology
In our study, we collected articles that have been published in popular scientific journals and provided the most important information to practitioners and researchers who investigate FAHP-related issues. To this end, an extensive search was carried out to find FAHP2 in titles, abstracts, keywords, and research methodologies of the papers. Our study attempts to document the high interest in FAHP, and provides a
Survey results
The structure of this section is built based on the chosen “Themes”, i.e. all reviewed papers that make use of FAHP for Selection purposes are presented in Section 4.1, for Evaluation purposes in Section 4.2, and so on. In each of these sections, papers are summarized in tabular formats considering the following dimensions: Application area, Other tool(s) used, Year and Specific objective (specific to the problem addressed in each paper). A unique identifier is also attributed to each paper in
Observations and concluding remarks
In an effort to provide a clear and comprehensive summary of the survey outcomes, Sections 5.1 to 5.6 present graphical and tabular representations, syntheses and discussions of the FAHP method as a developed decision making tool. Whenever possible, the survey outcomes are compared with findings and predictions from past MCDM-related surveys such as (Vaidya & Kumar, 2006) (AHP-related survey), (Mardani et al., 2015) (Fuzzy MCDM-related survey) and (Behzadian, Khanmohammadi Otaghsara, Yazdani, &
Open FAHP/FPCMs testbed
As it has been explained in the introduction section, this state-of-the-art survey started from a personal motivation around the study of a new consistency index for FPCMs’ weight derivation, for which we started the collection of FPCMs from various scientific journal papers with the objective to use them as basis of a testbed to evaluate the proposed consistency index. This gave us the idea to make all those FPCMs’ available online (for free) to various communities of researchers. In total,
State-of the-art survey’s conclusion
Because human decision-making contains fuzziness and vagueness, FAHP is often applied to tackle MCDM problems. Although a number of criticisms have been levelled against FAHP, or more specifically against scientific methods used to derive weights from fuzzy pairwise comparison matrices (FPCMs), FAHP remains very popular. Its popularity is due to its flexibility to be combined with other techniques (e.g., TOPSIS, QFD, LP...) and its simplicity of implementation. Given the high number of
Acknowlegement
The research leading to this publication is supported by the National Research Fund Luxembourg under grant agreement n° 9095399, as well as the European Union’s H2020 Programme for research, technological development and demonstration under grant agreement n° 688203.
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