I am currently working on a project where a multi criteria decision making algorithm is needed in order to evaluate several alternatives for a given goal. After long research, I decided to use the AHP method for my case study. The problem is that the alternatives taken into account for the given goal contain incomplete data.
For example, I am interested in buying a house and I have three alternatives to consider. One criterion for comparing them is the size of the house. Let’s assume that I know the sizes of some of the rooms of these houses, but I do not have information about the actual sizes of the entire houses.
My questions are:
- Can we apply AHP (or any MCDM method) when we are dealing with incomplete data?
- What are the consequences?
- And, how can we minimize the presence of missing data in MCDM?
I would really appreciate some advice or help! Thanks!
If you still looking for answers, please let me answer your questions. Before the detail explain, I coludn't answer with a technical approach in programming language.
First, we can use uncertinal data for MCDM, AHP with statical method. As reducing lost of data, you can use deep learning concepts like entropy. The result of it will be get reliability by accuracy of probabilistic approach.
The example that you talked, you could find the data of entire extent in other houses has same extent of criteria. Accuracy will depend on number of criteria, reliability of inference.
To get the perfect answer in your problem, you might need to know optimization, linear algebra, calculus, statistics above intermediate level
I'm student in management major, and I would help you as I can. I hope you get what you want