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Hi, If I understand well : - you have only one ad at a time, displayed at least 10 minutes and at most 60 minutes, and I suppose that ads cannot be fractionned. - ads can be ordered by their "value" for one click The main objective is to maximize the total number of valuated clicks, e.g. "fill" the most valuable ads with clicks, So the problem is finding some good time partition in "living" periods for the most valuable ads as possible.This tends to put the most valuable ads in peaks, then continue to the next, find a remaining peak, etc etc ... But the problem not so obvious if because of min/max bound on the "living" periods. The question is : - do you have other constraints/criteria (preferences of ads in time, forbidden ad chains ...) To try to anser to your question, my feeling is that a local solver may be really good only if you first build a initial solution that is not so bad. Otherwise, as I suppose you have real big datasets, it may be too slow. You may have to decompose your problem first (by day), then redo a global optimization on agregated solution. Try to think about what your moves can be : - for instance, it could be a simple swap. If your amount of "100% sold" clicks is constant, if will only move the two ads. For that, it is easy to predict the gain of this move - but I guess you will also need a time-shifting move (move this ad for 5 minutes before), and this one will be tricky ... If your amount of "100% sold" clicks is constant, may be you can partition your time in slots (of 10 minutes min), and assign tasks sequentially in them. This way you don't need any more optimisation ... Le 30/12/2010 23:13, Dave Wellman a écrit : All, --
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