Decision making : which formula is closest to ideal

[#1]

I have a number of shortlisted formulas, all of them are in specifications. Now the challenge is to rank them and identify the best.

Each formula has about a dozen properties. So far I have tried TOPSIS ranking (Euclidean distance basis). It works somehow, but I believe there may be a better approach of decision makind with data.

Are there other methods you would recommend for ranking based on multiple properties ?

I would be happy to learn from your experience !

[#2]

Hi @RikkiTikkiTavi,

Ranking based on euclidian distances are a good idea. However, when you have competing responses/properties, there might be some tradeoffs to consider, and euclidian distances may "shrink" and hide these tradeoffs.

For this reason, I tend to prefer using Pareto front to detect optimal solutions/formulations based on different emphasis between responses/properties. There is no dedicated JMP platform, but there may be several options to do this:

• You can create a large candidate dataset thanks to the models fitted to the responses using the Simulator (Simulation Experiment) or by creating a large space filling design and adding the responses formula to your table. Then, you can Select Rows in Data Tables and choose the option Select Dominant . The resultant set of rows is called the Pareto Frontier.
• You can try to check if some responses/properties are not correlated with each others, to reduce the output space dimensionality. Then, using Principal Components analysis or non-linear dimension reduction techniques like Multivariate Embedding (UMAP), you can reduce your multivariate space in a 2D or 3Dimensional space. Plotting the formula in this reduced responses space really helps visualizing the best candidates and tradeoffs.
• You can also do the previous options but instead of using directly the responses, you can use the normalized euclidian distances to avoid that a response may have a too strong influence on the others, and check the correlations between the responses distances to optimum. If you can reduce the dimensionality, the visualization is again greatly facilitated:
  
  

Hope this other approach and few tricks may help you,