Analytic hierarchy process (AHP) is a decision analysis method which combines qualitative and quantitative methods to solve multi-objective complex problems. This method combines quantitative analysis with qualitative analysis, uses the experience of decision-makers to judge the relative importance of the standards to measure whether the objectives can be achieved or not, and reasonably gives the weight of each standard of each decision-making scheme, and uses the weight to calculate the priority of each scheme, which is more effectively applied to those problems that are difficult to be solved by quantitative methods.
Advantages of AHP:
- In the face of the comprehensive evaluation of the overall problem with hierarchical structure, it decomposes layer by layer, and then makes a comprehensive evaluation on the basis of multiple criteria
- Simple and practical, combining qualitative and quantitative methods
- Less quantitative data are needed
Disadvantages of AHP:
- We can only choose the best strategy from the given one, but can't give a new strategy
- The index system used in AHP needs the support of expert system. If the index given is unreasonable, the result will be incorrect
- Consistency check is needed when AHP is used for multi-layer comparison. If the consistency requirement is not met, the AHP method will lose its function
- AHP method needs to find the eigenvalues of matrix. For some ill conditioned matrices, the eigenvalues will have systematic error
The code had been written in AHP.py.
https://zhuanlan.zhihu.com/p/101505929?utm_source=wechat_session
https://baike.baidu.com/item/%E5%B1%82%E6%AC%A1%E5%88%86%E6%9E%90%E6%B3%95/1672?fr=aladdin
https://blog.csdn.net/mmm_jsw/article/details/84863416