Good morning,
I have a dataset with the following columns:
Customer, Location, Province, Latitude, Longitude, Order_frequency
468554,MONTANO LUCINO,CO,45.78544042433004,9.044272270080452,47
194486,VERONA,VR,45.44653781280715,10.994846118237755,39
…(5000 rows)

I need to find the coordinates of the best place to place a warehouse, in order to minimize distance and/or travel times to all customers, taking into account the frequency of orders.
Can anyone give me some advice on how to do this?
Thank you!

Welcome to KIME Forum.
Although this is not a direct answer to your question, I would still like to point you to KNIME’s GeoSpatial nodes. I think these nodes hold the key to solving your problem.

you can achieve this with different levels of detail, but I’d suggest to use a weighted average.

You take the number of orders/weight of orders/or whatever other driver to weight distances. This choice is up to you depending on what is more important to you.

Example 1 (number of orders): you have 1 customer in Milano ordering once a week and another one in Torino making one order per month: warehouse should be somewhere in Novara.

Example 2 (weight/value/volume of purchased goods): same as above, but orders from the customer in Torino is 10x the ones in Milano: warehouse should be placed somewhere near Torino, then.

Second choice is how you compute distance:

you can consider linear distance (quick and costless to compute, good for big datasets, but not precise)

you can use real distance (requires an API licence, real result but costly in terms of time and money)

you can just take linear distance and increase it by a factor (10-20%) to take into account that real road is slightly longer than linear distance (quick and costless but accuracy is based on the assumption your increasing factor is precise)