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For purposes of discuss lets assume we are using sum of weighted impedance (we are actually doing some post-processing to make things easier in our BI tool). I see where you are coming from with regards to fixing the facilities to make the solutions comparable. The reason this doesn't solve our issue is that we don't actually care what the facility locations are. Our actual objective is to determine the performance impact if the real world operation was/was not segmented (namely the difference in impedance). Thanks for all your help, Jake
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07-28-2017
04:01 AM
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Hi Jay, The total number of facilities chosen is the same between run 1 and run 2 (so yes, for example in run 1 we would solve for two, then run 2 we would solve for 1 in a and 1 in b). We are checking the sum of the impedance and the average impedance in run 1 vs run 2 (both are improved in the subsets). No we are not fixing any of the facilities at any point (not sure what this would show?). I think I understand the differences between the two problem types, for the sake of understanding lets stick to minimize impedance. (we are not setting an impedance cutoff, so were hoping that maximise capacitated coverage would be optimising purely the impedance whilst honouring capacity caps). As of yet, every run we have done has seen an improvement in the subset (which is why we are confused). Many thanks, Jake
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07-25-2017
09:19 AM
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Hi Jay, Thanks for taking the time to reply to my question. I've tried to make a picture that explains what I mean. I'm using location allocation (I've tried both maximise capacitated coverage and minimise impedance), I keep all the parameters the same between runs (this is being run in a Python script so is easy to do). The results are approximately 5% worse in scenarios without the breakdown (at this point we have tried quite a few runs with different setups). Hope this helps you understand our position. Thanks again, Jake
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07-25-2017
01:00 AM
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We are trying to create a fixed number of facilities across a region to service a bunch of demand points. After we ran the problem we summed the total amount of length in the problem. We then ran the problem again but with the facilities and demand points broken up into smaller regions. The problem is: when the total length in all of the smaller problems is summed together the result is lower than in the single larger problem. Our premise that makes us think this is a problem is: the larger problem contains the solutions of the smaller problems, so the algorithm should be at be able to at least match the solution. Is our premise wrong, or is there some limitation of the algorithm that we are not aware of? Many thanks.
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07-21-2017
01:59 AM
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