A New Approach of MCGDM: MARCOS-Based Alternatives Measurement with Ranking Under Linguistic Z Number Information and Their Application in the Selection of Logistics Distribution Cold Chain Centre

The current study focuses on finding a balanced solution that considers various alternatives and ranks them based on compromise. It aims to strike a middle ground that takes into account different factors and trade-offs. (MARCOS) in multi-criteria group decision-making (MCGDM) under the background of linguistic Z number information (LZNI). The LZNI of an alternative about attribute set is an ordered pair of linguistic terms (LTs) in two different linguistic term sets (LTS), where the second component describes how sure we are that the first component is. LTs prove valuable when handling situations where it’s challenging to assign precise numerical values to assess attributes. Instead of relying on exact measurements, LTs allow for more flexible and qualitative descriptions. Thus, the decision information based on LZNI is more efficient than the existing one. The method has several advantages: it takes into account both the ideal and anti-ideal solutions from the start, it measures the utility degree more accurately, it proposes a novel way to define and aggregate utility functions, and it can handle a large number of criteria and alternatives without losing stability. Finally, consider an illustrative scenario where we need to choose the optimal logistics distribution cold chain centre. The chapter validates the new method through an extensive sensitivity and comparative analysis. The sensitivity and comparative analysis confirm the reliability of the proposed MARCOS-based MCGDM method under LZNI.