Intuitionistic Linguistic Multiple Attribute Decision-Making with Induced Aggregation Operator and Its Application to...

Area of Science:

• Decision Sciences
• Operations Research
• Environmental Management

Background:

• Multiple Attribute Decision Making (MADM) methods are crucial for complex choices.
• Intuitionistic Linguistic (IL) environments handle uncertainty and vagueness in decision data.
• Low carbon supplier selection is vital for corporate sustainability and environmental goals.

Purpose of the Study:

• To develop novel induced aggregation operators for intuitionistic linguistic environments.
• To propose a new MADM approach utilizing these operators.
• To demonstrate the application of the approach in low carbon supplier selection.

Main Methods:

• Introduction of the intuitionistic linguistic weighted induced ordered weighted averaging (ILWIOWA) operator.
• Development of a generalized version: intuitionistic linguistic generalized weighted induced ordered weighted averaging (ILGWIOWA) operator.
• Application of these operators within a MADM framework.

Main Results:

• The proposed ILWIOWA and ILGWIOWA operators effectively aggregate intuitionistic linguistic information.
• The developed MADM approach provides a practical method for evaluating and selecting low carbon suppliers.
• Comparative analyses confirm the effectiveness and practicality of the proposed approach.

Conclusions:

• The novel aggregation operators offer enhanced capabilities for MADM in IL environments.
• The proposed method is effective for addressing the complexities of low carbon supplier selection.
• This research contributes to more informed and sustainable supply chain management decisions.