3
Imagine the following scenario: a gas station is fined for tax evasion for issuing tax coupons already issued - what happens is that each vehicle of the companies contracted to the station supplied and at the end of the month the post issued a tax note along with the respective invoice.
The operation, however, was done wrong for almost 2 years. There was no tax evasion under any circumstances, however, tax coupon numbers were not referenced to tax bills.
We are talking about an immense mass of data, where we basically need a match for amount of liters per fuel type x value - for example: a tax note of R $ 32.127,12 e 19.047,61 Liters of diesel oil has to be "regrouped" with N tax coupons.
However, we have the following problems: fuel prices vary, because the tax bill can be the combination of N pumps x N tax printers, that is, we are talking about a stratospheric mass of data.
However, knowing that we can limit the recombination "search" radius by date (last 30 days) - (which in data volume sums up to trillions of combinations in this period), could we use some tree algorithm? Or some variant algorithm of the traveling salesman?
I was able to solve part of the problem with an algorithm implementation! Thank you
– Allyson de Paula