Cutting Stock Solver

Cutting Stock Solver

Filipe Brandao

@AMPL

Mathematics in Real Life

Mathematics in Real Life

Description

We present a multiple-choice vector packing solver, based on an arc-flow formulation with side constraints and graph compression, for solving bin packing and cutting stock problems.

Short Summary

The VPSolver generates very strong models (equivalent to Gilmore and Gomory’s) that can be solved using general-purpose mixed-integer programming solvers such as Gurobi and GLPK.

When & Why?

The p-dimensional vector 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.

In practice, this problem models, for example, static resource allocation problems where the minimum number of servers with known capacities is used to satisfy a set of services with known demands. The multiple-choice vector bin packing problem is a variant of the vector packing problem in which bins have several types (i.e., sizes and costs) and items have several incarnations (i.e., will take one of several possible sizes).

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The expertise hub is a bridge between experts and beginners, academia and industry, businesses and policymakers. Sharing knowledge creates a ripple effect, empowering more people, facilitating innovation, and leading to smarter decisions. Small steps can make a huge impact! 

Whether you’re here to learn, share, or collaborate, you’re in the right place.


2025 © Optimization4All

2025 © Optimization4All