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I'm trying to understand how I would approach a problem aiming to optimize a sequence of events to maximize another variable. For example, say I start selling a t-shirt at multiple stores, where each store refers to a varying combination of other stores to determine their price. For this problem, assume my goal is to keep the price as high as possible. This problem is relatively easy with a small number of stores, but as you expand into a large number of stores the number of combinations of sequences becomes very large.

One could let the computer run and calculate all possible combinations, but if one wanted to quickly see how different scenarios change the outcome, how could this problem be approached? Are there deep-learning techniques or other algorithms I'm not aware of that may be helpful? I'm open to algorithms in Python and R.

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    $\begingroup$ This sounds like a straightforward optimization problem. Possibly an NP-hard one, but with known inputs. Is there anything random or statistical about it? If not, this is off-topic here. Optimization in general is a vast field; you could start by looking at greedy algorithms. $\endgroup$ – Stephan Kolassa Sep 22 '18 at 14:07

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