Filipe Brandao
@AMPL
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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