Throughout my thesis I use JuMP, a modeling language for mathematical optimization embedded in Julia. On this page I present the code for two examples that I use in the introduction of my thesis. The material below is free to use in other lectures or programs with reference to the original source.
Julia code (click to download):
The raw code from the small robust optimization example is also given below to copy-paste.
# Models for introduction thesis Frans de Ruiter, 2017 CC.
# Load packages JuMP and wrapper for Gurobi
using JuMP, Gurobi
ro = Model(solver=GurobiSolver()) # Define robust optimization model with solver
@variable(ro, x[1:2] >= 0) # Add variable
@objective(ro, Max, 5*x[1] + x[2]) # Add objective
@constraint(ro, 21.94174*x[1] + 4.38476*x[2]
+ 1/sqrt(2)*norm(x) <= 200) # Add (robust counterpart) constraint
solve(ro) # Solve the model
obj_ro = getobjectivevalue(ro) # Obtain the objective value and solution
x_ro = getvalue(x)
#----------------------------------------
# The same as above, but now for the nominal model
nom = Model(solver=GurobiSolver()) # Define nominal optimization model with solver
@variable(nom, xnom[1:2] >= 0) # Add variable
@objective(nom, Max, 5*xnom[1] + xnom[2]) # Add objective
@constraint(nom, 21.94174*xnom[1] +
4.38476*xnom[2] <= 200) # Add nominal constraint
solve(nom) # Solve the model
obj_nom = getobjectivevalue(nom) # Obtain the objective value and solution
x_nom = getvalue(xnom)