A novel alpha-level sets based fuzzy DEMATEL method considering experts’ hesitant information

Highlights

• A novel way for handling fuzzy assessments in fuzzy DEMATEL methods is proposed.

• Two options for analyzing the interrelationship among factors are provided.

• Expert’s hesitant information in fuzzy DEMATEL method is considered.

Abstract

Fuzzy DEMATEL method is diffusely employed to analyze the interrelationship among factors in various fields under uncertain and fuzzy environment. In the extant fuzzy DEMATEL studies, fuzzy assessments are converted into related crisp values by means of different defuzzification ways. Such conversion may result in not only the loss of information, but also the discount of consideration of fuzzy information. Therefore, it seems reasonable and necessary to keep as much information as possible during the analysis process, instead of converting it into crisp values at the very beginning. An appropriate way to keep as much information as possible is to employ alpha level sets to handle fuzzy information instead of simple defuzzification. In addition, it is quite common that experts may hesitate under uncertain and fuzzy environment in the real-world situation, when providing their assessments on the interrelationship among factors. However, such practical issue is neglected in the extant fuzzy DEMATEL methods. With respect to aforementioned limitations, this study proposes a novel alpha level sets based fuzzy DEMATEL method to handle the fuzzy information, together with considering experts’ hesitation from qualitative context under uncertain and fuzzy environment. Additionally, two different options are provided to analyze the interrelationship among factors in the proposed method. The proposed method not only improves the extant fuzzy DEMATEL studies, but also enriches the theoretical studies of fuzzy DEMATEL, its novelty and superiority is demonstrated through illustrative example and related comparisons.

Introduction

DEMATEL (DEcision-MAking Trial and Evaluation Laboratory) method (Gabus and Fontela, 1972, Gabus and Fontela, 1973) is a very popular and well-known multi-criteria decision making (MCDM) method that is employed to analyze interrelations among different factors, and visualize its cause–effect relationship.

Since the DEMATEL method was proposed, it has been diffusely employed to identify critical factors and analyze cause–effect relationship in different real-world decision problems, such as hospital service quality (Shieh, Wu, & Huang, 2010), supplier selection (Mirmousa & Dehnavi, 2016), disaster operations management (Celik, 2017), diffusion of electric vehicles (Liu, You, Xue, & Luan, 2017), supply chain management (Lin et al., 2018, Song et al., 2017), emergency management (Ding and Liu, 2018, Zhou, Huang et al., 2011, Zhou et al., 2017), sustainable solid waste management (Abdullah, Zulkifli, Liao, et al., 2019), safety management (Yazdi, Khan, Abbassi, et al., 2020), performance management (Jiang, Shi, Lin, & Liu, 2020), smart city determinants (Braga et al., 2021) and so on. It is a challenge for the DEMATEL method (Gabus and Fontela, 1972, Gabus and Fontela, 1973) to tackle complex decision problems under uncertain and fuzzy environment, in which the judgments provided by experts are usually expressed by crisp values. However, the crisp values cannot express the vagueness and uncertain (Bellman and Zadeh, 1970, Zadeh, 1975) of the real-world problems comprehensively and properly.

To handle the complex decision problems, the DEMATEL method has been expanded to uncertain and fuzzy environment (Bai and Sarkis, 2013, Wu and Lee, 2007), in which various information types are employed to model the uncertainty, such as linguistic information (Li et al., 2020, Wu and Lee, 2007), interval-valued intuitionistic fuzzy information (Abdullah et al., 2019), 2-dimension uncertain linguistic information (Ding & Liu, 2018), 2-tuple linguistic information (Liu et al., 2017), linguistic Z-number information (Jiang et al., 2020), intuitionistic fuzzy information (Büyüközkan, Güleryüz, & Karpak, 2017), D number information (Zhou et al., 2017), interval type-2 fuzzy information (Baykasoğlu and Gölcük, 2017, Dinçer et al., 2019, Tooranloo et al., 2017) and so on.

Extant fuzzy DEMATEL methods (Bai and Sarkis, 2013, Jiang et al., 2020, Li et al., 2020, Wu and Lee, 2007) deal with the fuzzy information from different aspects and produce rich results, they enrich and extend the theoretical studies of the DEMATEL, as well as make great contributions to analysis on the interrelations among different factors under uncertain and fuzzy environment. Despite this, extant fuzzy DEMATEL methods convert fuzzy input information into crisp values (Wu and Lee, 2007, Zhou, Huang et al., 2011) by means of defuzzification to obtain initial direct-relation matrix. Such a transformation might lead to the discount of considering fuzzy input information, it might lose important information during the defuzzification process (Wang and Elhag, 2006, Wang et al., 2020).

Thus, it seems necessary to handle the fuzzy input information from more reasonable perspective, rather than to convert it into single crisp value at the very beginning. Additionally, although the existing fuzzy DEMATEL methods have considered the fuzzy input information from quantitative or qualitative context, they neglect a fact that experts with different knowledge, experiences and background are usually bounded rational who might hesitate when providing their assessments or judgments (Chen et al., 2021, Chen et al., 2022, Rodriguez et al., 2012). Hesitancy is an inevitable and practical issue in our daily life (Rodriguez et al., 2012), and should be considered particularly under uncertain and fuzzy environment.

To overcome the limitations in the extant fuzzy DEMATEL studies, this paper presents a novel alpha level sets based fuzzy DEMATEL method. It tackles the fuzzy input information from the perspective of alpha level sets, instead of defuzzification. Meanwhile, this paper handles the experts’ hesitant information from qualitative context by employing hesitant fuzzy linguistic term sets (HFLTS), because of its closeness to humans natural language as well as easy to be understood.

The rest structure of this paper is: Preliminary knowledge, including fuzzy DEMATEL method, HFLTS and the principle of alpha level sets, will be presented in Section 2. The proposed method will be presented in Section 3. Section 4 demonstrates the validity and superiority of the proposed method through illustrative example, comparisons and related discussions. The conclusions and future works are offered in Section 5.