# The effect of choosing big M properly

I have a set of linearized constraints that are modelled using big-Ms. Now, it is, of course, common knowledge to make the value of M and small as possible in order to provide tighter LP relaxations of the (e.g.) MIP we are solving.

I am looking for some examples where this tightening of the 'M' is really useful from a computational perspective. As often by my experience, the smallest value of M is so trivial that it does not really influence computational performance (Equal to the cardinality of a set; maximum length of a time horizon etc. )

• No worries. I'm going to delete my comment. Jun 3, 2019 at 22:02

The bigger the big-M is, more likely the numerical issues will happen with solvers.

If you have right hand sides around $$10^{10}$$ and objective function coefficients in the range of $$10^{-2}$$, then solvers will have hard time dealing with such big range of values. And big-M's are the usual suspects in such situations.

So smaller the big-M, tighter and more numerically stable the matrix is.

One option when dealing with such big bigM values is using indicator constraints. They are great ways to write if->then type of logic in MIP programming. See examples of indicator constraints for Xpress Mosel here.

I often see people set $$M$$ to something like $$10^{12}$$, when the rest of the model is on the order of $$10^2$$, because they got the message that $$M$$ should be "a large constant". Reducing $$M$$ to something several orders of magnitude smaller then does have a noticeable impact on the run time.

My point is: Once you know that you should should be careful about choosing $$M$$, it might not make much difference if you set it to $$10^3$$ or $$10^4$$, and it might not be worth investing too much time to get it just right.

But if you are still in the frame of mind that says "set $$M$$ as large as you want", there can be a huge difference when you bring $$M$$ down from the stratosphere and make it something reasonable.

The practical study Analysis of Strength and Weaknesses of a MILP Model for Revising Railway Traffic Timetables includes an analysis of the influence of big M constraints. The conclusion is mixed, though: in their model, knowledge of sharp M-values has a notable effect, but sharp values are obviously hard to find in practice.

I've run into issues with this for supply chain problems choosing which facilities to open. In my model, trucks to deliver to customers could only come from open facilities, so big M had to be larger than the total number of trucks leaving the facility.

For my real-world problem, there were more than 10,000 trucks (my original choice of M).

• So; did you notice significant computational differences if you set the M larger than its smallest value? If so; I would be interested in the model :) Jun 1, 2019 at 23:02
• I just gave it a test (this was work for a client of mine). The initial test for that M in the model made no difference time-wise whether it was the minimum value vs being 100 times that value. I tried blowing up all three big Ms in my model, but saw no difference in computational time for my particular problem instance. Jun 1, 2019 at 23:29
• I will add - choosing M not big enough for some of my client's instances did cause problems. So in the future I'll be erring on the side of a bigger M than I think I need. Jun 1, 2019 at 23:30
• I usually set M at a value 2-3x the largest potential LHS value. That helps avoid computational issues while making sure the constraint does its job Jun 3, 2019 at 22:00
• The lesson for me was mostly a reminder that customers needs may change. The initial M was plenty, but then the data changed :) Jun 3, 2019 at 22:07