New Model Blues

Recently we started working on a project for a client that incorporates a substantial  amount of complexity into our current optimization system. This will replace a process where this client had a second system that ran before ours and created a sub-optimal solution for this process. By incorporating it into our process we should be able to create a more optimal solution by leveraging the increased knowledge we have when our optimization system is run.

There is just one problem with adding all of this complexity; they don’t want the run time to increase.  This is the part that is causing my new model blues. I’m trying to find the best trade off, because I can find a better solution but the run time exceeds the amount of time that the data is valid or I can create a solution that is roughly in line with what was being done in separate systems previously in about the same run time.

Have any of you run into a similar issue? Were you able to find a suitable trade off?

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2 Responses to New Model Blues

  1. Sebastian says:

    Could you look at ways to improve the current system’s run time?
    Before you had not the run time issue, but now that you have it you may be able to find an efficient route to get the optimal result.

    Also, is there a way you can build up one calculation of optimal solution on a previous one so as to reduce run time?

    Best regards

    • Robert Randall says:

      That is a method we could use and is something we will definitely try. This system is not new and throughput has been tweaked many times over the last three years when we wanted to put new features in without increasing the run time. So I would like to think that there isn’t much more speed that we can squeeze out of the current system.

      What we’re currently trying to do is to limit excess existing complexity that doesn’t bring value to the output and replace that complexity with the new model.

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