Abstract
Artificial intelligence (AI) is the most popular technology for searching the natural essence of human beings' intelligence. AI is influencing the paradigm of the enterprise management and product optimization. The development of AI in enterprise management provide an opportunity for all enterprises to construct automated business processes, improve the customer experience, and expand the product differentiation. Nowadays, the changeable world increases the level of difficulty to make an appropriate AI strategy for a company because of the information uncertainty and complexity. Probabilistic dual hesitant fuzzy set (PDHFS), which is a very effective tool to handle uncertain information, contains the hesitant fuzzy information and the corresponding probabilistic information. Standing in front of the more and more complex evaluation/selection problems, decision makers (DMs) could express their preference information more flexibly using the probabilistic hesitant fuzzy information. In this paper, we focus on the strategy selection problem in AI and solving it by a proposed integrated AHP and VIKOR method under probabilistic dual hesitant fuzzy information. First, we construct the probabilistic dual hesitant fuzzy comparison matrix (PDHFCM) and propose a specific transformation function for using AHP method under probabilistic dual hesitant fuzzy information. For completing the AHP, we redefine a consistency measure and propose an appropriate information-improved approach to obtain the consistent comparison matrix and the corresponding weight values simultaneously. In addition, we study the properties of PDHFS deeply and propose a new comparison method and a novel distance measure for PDHFS to distinguish the different probabilistic hesitant fuzzy information effectively. Then, we propose an integrated VIKOR and AHP method and use the method to solve the AI strategy selection problem. Finally, the availability and effectiveness of the proposed method are illustrated by a case on AI strategy selection.
Highlights
Artificial Intelligence (AI) is the state-of-the-art science for studying and developing some theories, methods, techniques and applied systems to simulate, stretch and extend the human intelligence
DUAL HESITANT FUZZY SET Considering the complexity of the world, to express the uncertain information from different viewpoints accurately, Zhu et al introduced the concept of DHFS [7], [8], which contains the characteristics of the hesitant fuzzy set [6] (HFS) [6] and the intuitionistic fuzzy set [5] (IFS) [5]
Making full use of the characteristics of Probabilistic dual hesitant fuzzy set (PDHFS) and combining the advantages of the VIKOR method and AHP, in this paper, we propose an integrated VIKOR and AHP method under probabilistic dual hesitant fuzzy information
Summary
Artificial Intelligence (AI) is the state-of-the-art science for studying and developing some theories, methods, techniques and applied systems to simulate, stretch and extend the human intelligence. In order to consider different viewpoints (membership degrees and non-membership degrees) in the process of decision-making, the intuitionistic fuzzy set [5] (IFS) was proposed to assist DMs in expressing their evaluations. Considering the hesitant situation when DMs could not make a determination between several values in decisionmaking, the hesitant fuzzy set [6] (HFS), which provides an information form containing all uncertain information, was proposed and has been widely used in multi-criteria decision-making problems. In order to solve the problem and help DMs to express their evaluations more flexibly and accurately, Zhu and Xu [9] added the probability information in HFS and proposed the concept of probability-hesitant fuzzy set (PHFS). The main contribution is to propose an integrated VIKOR and AHP method under probabilistic dual hesitant fuzzy information for solving MCGDM problems.
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