An approach to hesitant fuzzy linguistic multiple criteria group decision making with uncertain criteria weights considering incomparability between alternatives

Abstract

In multi-criteria group decision-making (MCGDM) problems, the consensus of experts is taken as the result, and it is common that the result may include incomparability between alternatives. The longest ranking of alternatives that attains consensus and allows the incomparability between alternatives is called a maximum consensus sequence (MCS). In decision-making problems related to emerging industry, it is hard to accurately obtain criteria weights. In this case, how to weight different criteria values and individual opinions to mine an MCS is worth studying. This article applies the hesitant fuzzy linguistic ORESTE method to obtain the ranking of alternatives, allowing for uncertain criteria weights and incomparability between alternatives. Then, a compromise-or-not mechanism based on uncertain criteria weights, which can derive experts’ attitudes towards collective result, is developed to tackle the consensus issue. Two kinds of conflicting items about the incomparability in the collective result and divergent opinions of uncompromising experts are identified. Through information exchange among experts regarding the conflicting items, an MCS can be obtained. The proposed method provides an inspiration for MCGDM in emerging industries.

Keywords:

• Multi-criteria analysis
• group decision-making
• maximum consensus sequencce
• incomparability
• emerging industry
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Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China (Nos. 71771156, 71971145, 72171158).