Vfrdtyky

I'm making an application to find the best products to buy based on several criteria. can be called a decision support system.

some examples of the criteria I use are:

1. location, the more the sending location is with my city, the better.
   I have determined the weight of the location, I determine the weight
   of my city with a value of 100, the farther the shipping city with
   my city, then the weight will be smaller.


2. the number of reviews owned by a product, more means better


3. rating value, the higher the rating, the more means better


4. price, the cheaper the price the better

I was recommended to use a method called AHP, I have read about AHP and although I think AHP is a good method, in my opinion what I want can not be fulfilled entirely with AHP because it does not take into account the nominal value of the rating and price, it only counts one thing importance to another

my questions are :

• with the requirements of the criteria, what MCDM method should I use?




• Does AHP actually can accommodate my needs? if yes, how? is it using Fuzzy-AHP? if so, I will start learning Fuzzy and things related to it

Thanks for the question. So, AHP*1 is a method used in decision-making (DM) to methodically assign weights to the different criterion. In order to score, rank and select the most-desirable alternative you need to complement AHP with another MCDC method that fulfils those tasks.

There several methods to do that. TOPSIS and ELECTRE, for instance, are commonly used to that purpose. *2-3. I leave you a link on the papers and tutorials of those methods so you understand how they work. -- SEE RESOURCES.

In regards to using fuzzy logic in AHP. While there are several proposals on using FAHP*4, Saaty himself, creator of the AHP states that this is redundant*5-7 since the scale in which criteria are assessed to weighing in AHP already operates with a fuzzy logic.

However, in the case, your criteria are based on qualitative data and therefore you are dealing with uncertainty and potentially, incomplete information, you can use fuzzy numbers in TOPSIS for those variables. You can check the tutorials in resources to understand how to apply those methods.

In recent years, some researchers have argued that fuzzy TOPSIS only considers the membership function. (That is, the closest an imprecise parameter is to reality) and ignores the non-membership and indeterminacy degree *9-10, so how false and not determinable is that parameter. The neutrosophic theory was mainly pioneered by *10 Smarandache.
So, in response, nowadays, neutrosophic TOPSIS is being to be used to deal with uncertainty. I recommend reading the papers below to understand the concept.

So, in summary, I will personally recommend applying AHP and Fuzzy or Neutrosophic TOPSIS to address your problem.

Resources:

• Manoj Mathew. Tutorial Youtube FAHP. Fuzzy Analytic Hierarchy Process (FAHP) - Using Geometric Mean. Retrieved from: https://www.youtube.com/watch?v=5k3Wz1AfVWs
  


• Manoj Mathew. Tutorial Youtube FTOPSIS. Fuzzy TOPSIS. Retrieved from: https://www.youtube.com/watch?v=z188EQuWOGU
  


• Manoj Mathew. TOPSIS - Technique for Order Preference by Similarity to Ideal Solution Retrieved from: https://www.youtube.com/watch?v=kfcN7MuYVeI
  


• MCDC in R: https://www.rdocumentation.org/packages/MCDA/versions/0.0.19
  


• MCDC in JS: https://www.npmjs.com/package/electre-js
  


• MCDC in Python: https://github.com/pyAHP/pyAHP
  

REFERENCES:

• 1 Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3-5), 167.
  doi:10.1016/0270-0255(87)90473-8


• 2 Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer, Berlin, Heidelberg.


• 3 Figueira, J., Mousseau, V., & Roy, B. (2005). ELECTRE methods. In Multiple criteria decision analysis: State of the art surveys (pp. 133-153). Springer, New York, NY.


• 4 Mardani, A., Nilashi, M., Zavadskas, E. K., Awang, S. R., Zare, H., & Jamal, N. M. (2018). Decision Making Methods Based on Fuzzy Aggregation Operators: Three Decades Review from 1986 to 2017.
  International Journal of Information Technology & Decision Making, 17(02), 391–466. doi:10.1142/s021962201830001x


• 5 Saaty, T. L. (1986). Axiomatic Foundation of the Analytic Hierarchy Process. Management Science, 32(7), 841.
  doi:10.1287/mnsc.32.7.841


• 6 Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3-5), 167.
  doi:10.1016/0270-0255(87)90473-8


• 7 Aczél, J., & Saaty, T. L. (1983). Procedures for synthesizing ratio judgements. Journal of Mathematical Psychology, 27(1), 93–102. doi:10.1016/0022-2496(83)90028-7


• 8 Wang, Y. M., & Elhag, T. M. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert systems with applications, 31(2), 309-319


• 9 Zhang, Z., Wu, C.: A novel method for single-valued neutrosophic multi-criteria decision making with incomplete weight information. Neutrosophic Sets Syst. 4, 35–49 (2014)


• 10 Biswas, P., Pramanik, S., & Giri, B. C. (2018). Neutrosophic TOPSIS with Group Decision Making. Studies in Fuzziness and Soft Computing, 543–585. doi:10.1007/978-3-030-00045-5_21


• 10 Smarandache, F.: A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Setand Logic. American Research Press, Rehoboth (1998)