Shared manufacturing service evaluation based on intuitionistic fuzzy VIKOR.

Area of Science:

• Manufacturing Systems Engineering
• Operations Research
• Decision Sciences

Background:

• The sharing economy has spurred interest in shared manufacturing models.
• Evaluating shared manufacturing services requires a robust framework to handle complex quality of service (QOS) indicators.

Purpose of the Study:

• To develop a VIKOR-based evaluation approach for shared manufacturing services within an intuitionistic fuzzy environment.
• To enable effective filtering of acceptable services from a large pool based on multiple criteria.

Main Methods:

• Constructed a QOS evaluation index system with online and offline indicators.
• Developed a service recommendation model incorporating supply and demand constraints.
• Applied intuitionistic fuzzy numbers, G1-method, variable-precision rough set theory, and maximum entropy theory for weight determination.
• Utilized the VIKOR method with intuitionistic fuzzy sets for service evaluation and ranking.

Main Results:

• The proposed approach effectively evaluates and ranks shared manufacturing services.
• Demonstrated the combined benefits and minimum regret values using intuitionistic fuzzy number similarity.
• Verified the algorithm's reliability and feasibility through a real-world case study.

Conclusions:

• The intuitionistic fuzzy VIKOR method provides a reliable framework for shared manufacturing service evaluation.
• The approach successfully addresses the multi-indicator, multi-stakeholder decision-making challenges in shared manufacturing.
• This methodology aids users in selecting optimal services from diverse offerings.