Multicriteria Group Decision-Making Using the LogTODIM-TOPSIS Approach in a Linguistic Z-number Environment for Selecting Auto Parts Materials in the Technology of Automobile

In the realm of automobile design, materials, and manufacturing processes, improving safety and sustainability is paramount. Among these factors, the selection of auto parts materials stands out as a critical link. The choices made in material selection not only impact the safety and durability of cars but also influence developers in designing higher-quality auto products. Consequently, the question of how to choose auto parts materials has become a central concern for customers, designers, and manufacturing managers alike. To address this challenge, the study introduces the Logarithmic TODIM (LogTODIM) and TOPSIS techniques for multiple-criteria group decision-making (MCGDM) in the context of automobile technology. Additionally, the use of linguistic Z numbers (LZN) proves effective in handling uncertain and ambiguous information during auto parts materials evaluations. The proposed LZN LogTODIM-TOPSIS (LZN-LogTODIM-TOPSIS) technique offers a robust approach for MCGDM under LZN conditions. A numerical example validates the effectiveness of this model, highlighting its contributions: (1) Extending LogTODIM-TOPSIS to LZNs. (2) Applying the entropy method to determine attribute weights based on LZN information. (3) Introducing LZN-LogTODIM-TOPSIS for MCGDM in ILZN environments. (4) Presenting a comparative case study for auto parts materials evaluation in automobile technology.