Summary of Shadowed Ahp For Multi-criteria Supplier Selection, by Mohamed Abdel Hameed El-hawy
Shadowed AHP for multi-criteria supplier selection
by Mohamed Abdel Hameed El-Hawy
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel method, Shadowed AHP, to address Analytical Hierarchical Process (AHP) problems that involve preference values represented by uncertain numbers and linguistic information at multiple granularities. Building on previous work, this method utilizes shadowed fuzzy numbers (SFNs), which preserve uncertainty properties and approximate different types of fuzzy numbers. The proposed approach converts multi-granular preference values into a unified SFN model and leverages their properties to perform ranking and aggregation. This methodology is demonstrated through a supplier selection problem that relies on multi-granular information, showcasing the significance of Shadowed AHP for decision-making applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make decisions when you have lots of different types of information that are not very certain. They use something called shadowed fuzzy numbers to help sort through this information and make a good choice. It’s like trying to pick the best supplier for your company, but you have many different opinions and ratings from different people. This new method helps take all those opinions and ratings into account and makes a decision that works well. It’s important for making decisions in business and other areas where there is not one clear answer. |
