[<img alt="build status" src="https://img.shields.io/github/workflow/status/modulitos/bin_packer_3d/CI/master?style=for-the-badge" height="20">](https://github.com/modulitos/bin_packer_3d/actions?query=branch%3Amaster)
# bin_packer_3d
This crate solves the problem of "fitting smaller boxes inside of a larger box" using a three
dimensional fitting algorithm.
The algorithm orthogonally packs the all the items into a minimum number of bins by leveraging a [First Fit
Decreasing](https://en.wikipedia.org/wiki/Bin_packing_problem#First_Fit_Decreasing_(FFD)) greedy
strategy, along with rotational optimizations.
# Usage:
```rust
use bin_packer_3d::bin::Bin;
use bin_packer_3d::item::Item;
use bin_packer_3d::packing_algorithm::packing_algorithm;
let deck = Item::new("deck", [2, 8, 12]);
let die = Item::new("die", [8, 8, 8]);
let items = vec![deck, deck, die, deck, deck];
let packed_items = packing_algorithm(Bin::new([8, 8, 12]), &items);
assert_eq!(packed_items, Ok(vec![vec!["deck", "deck", "deck", "deck"], vec!["die"]]));
```
# Limitations:
This algorithm solves a constrained version of the 3D bin packing problem. As such, we have the
following limitations:
* The items we are packing, and the bins that we are packing them into, are limited to cuboid
shapes.
* The items we are packing can be rotated in any direction, with the limitation that each edge must
be parallel to the corresponding bin edge.
* As an NP-Hard problem, this algorithm does not attempt to find the optimal solution, but instead
uses an approximation that runs with a time complexity of *O(n^2)*
# Acknowledgements:
The algorithm leverages a rotational optimization when packing items which are less than half the
length of a bin's side, as proposed in the paper titled "The Three-Dimensional Bin Packing Problem"
(Martello, 1997), page 257:
[https://www.jstor.org/stable/pdf/223143.pdf](https://www.jstor.org/stable/pdf/223143.pdf)