The p-dimensional vector bin packing problem, also called general assignment problem, is a generalization of bin packing with multiple constraints. In this problem, we are required to pack n items of m different types, represented by p-dimensional vectors, into as few bins as possible. By means of reductions to vector packing, several cutting & packing problems, including the one-dimensional bin packing and cutting stock problems, can be solved.

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**By means of reductions to vector packing**, VPSolver can be used to solve several problems such as:

- Bin packing;
- Cutting stock;
- Cardinality constrained bin packing;
- Cutting stock with cutting knife limitation;
- Bin packing with conflicts;
- Cutting stock with binary patterns [01CSP Report];
- Cutting stock with binary patterns and forbidden pairs.
- [Computational results on several benchmark test data sets]

**By means of reductions to multiple-choice vector packing**, VPSolver can be used to solve several problems such as:

- Variants of the problems from the list above that consider multiple bin types;
- Variable-sized bin packing (as an one-dimensional multiple-choice vector bin packing problem).

**VPSolver includes a python interface that allows modeling other problems easily.** Using the python interface, VPSolver can be used to solve problems such as:

- Many variants that happen in the industry that include cutting and packing problems as subproblems of a larger production planning problem;
- Multi-stage variants (e.g, two- and three-stage two-dimensional cutting stock problems);
- Multi-period variants (e.g., plan the production for several days with the possibility of delaying or anticipating the production of some items).

Note: **Suggestions of other cutting & packing problems (including industrial applications) are welcome!** [Contact]