Facility location: results for a nonlinear model with random instances


Results obtained using Gurobi for solving a nonlinear Facility Location Problem (FLP), using the models described in Mathematical Optimization: Solving Problems using Python and Gurobi.

Parameters used: number of facilities is 10% of the number of customers.

CPU time limited to 300 seconds. (Click on values for selecting data to display.)

Benchmark instances analysed
Instance familyTypeDescription
Num.Int=2 random Number of linear segments in the approximation: 2
Num.Int=5 random Number of linear segments in the approximation: 5
Num.Int=10 random Number of linear segments in the approximation: 10
Num.Int=20 random Number of linear segments in the approximation: 20
Num.Int=50 random Number of linear segments in the approximation: 50
Num.Int=100 random Number of linear segments in the approximation: 100
Num.Int=200 random Number of linear segments in the approximation: 200
Num.Int=500 random Number of linear segments in the approximation: 500
Num.Int=1000 random Number of linear segments in the approximation: 1000