Decision making : which formula is closest to ideal

RikkiTikkiTavi — Wed, 16 Sep 2026 08:32:33 GMT

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 !

Re: Decision making : which formula is closest to ideal

Victor_G — Wed, 16 Sep 2026 13:18:26 GMT

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,

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Decision making : which formula is closest to ideal

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