tex: NFBP

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Paul ALNET 2023-06-03 14:22:45 +02:00
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@ -104,12 +104,34 @@ conduct our own analysis and study various algorithms and their probabilistic
advantages, focusing on one-dimensional bin packing, where we try to store
items of different heights in a linear bin.
\section{Next Fit Bin Packing algorithm}
\section{Next Fit Bin Packing algorithm (NFBP)}
Our goal is to study the number of bins $ H_n $ required to store $ n $ items
for each algorithm. We first consider the Next Fit Bin Packing algorithm, where
we store each item in the next bin if it fits, otherwise we open a new bin.
\paragraph{} Each bin will have a fixed capacity of $ 1 $ and items and items
will be of random sizes between $ 0 $ and $ 1 $. We will run X simulations % TODO
with 10 packets.
\subsubsection{Variables used in models}
\subsubsection{Complexity and implementation optimization}
The NFBP algorithm has a linear complexity $ O(n) $, as we only need to iterate
over the items once.
When implementing the statistical analysis, the intuitive way to do it is to
run $ R $ simulations, store the results, then conduct the analysis. However,
when running a large number of simulations, this can be very memory
consuming. We can optimize the process by computing the statistics on the fly,
by using sum formulae. This uses nearly constant memory, as we only need to
store the current sum and the current sum of squares for different variables.
% TODO : code
% TODO : move this somewhere else ?
% TODO : add a graph
\cite{hofri:1987}
% TODO mettre de l'Histoire