Mathematical Optimization

Solving Problems using Gurobi and Python


Kubo, Pedroso, Muramatsu and Rais


In this page we report results obtained using Python/Gurobi models for solving several well-known problems.  The computational setup was the following:

  1. Python version: 2.6.6

  2. Gurobi version: 5.0.1

  3. Computer characteristics:

  4. - Intel(R) Xeon(R) CPU E5-2687W 0 @ 3.10GHz

  5. - RAM: 64 GB

  6. - CPU usage limited to one thread

Case studies:

  1. 1.Knapsack problem:

       Multi-constraint knapsack problem

  1. 2.Location

        Facility location problem -- ORLIB instances

        Facility location problem -- random instances

        Comparison of k-median, k-center, and binary search with k-cover

        Nonlinear facility location problem

  1. 3.Graph problems

        Graph partitioning

        Stable set

        Graph coloring

  1. 4.Routing

        Traveling salesman problem

        Asymmetric traveling salesman problem

        Traveling salesman problem with time windows

        Vehicle routing problem

  1. 5.Scheduling

        Scheduling with linear ordering formulation, time index formulation, and disjunctive formulation

  1. 6.Lot sizing problem

        Lot sizing using Trigeiro's-like instances

  1. 7.Piecewise linear approximation of nonlinear functions

        Nonlinear facility location problem

  1. 8.Multiobjective optimization

  2. 9.Second-order cone optimization

        Weber problem