The DSPsolver.jl package provides an interface for using an open-source software package DSP for solving stochastic mixed-integer programming problems from the Julia language. You need to download and install DSP package.
This package requires the StochJuMP.jl and MPI.jl packages for Julia. The StochJuMP.jl package provides a scalable algebraic modeling tool for stochastic programming. The MPI.jl package enables the Julia script to run on a distributed computing system via MPI communication.
You can install DSPsolver.jl using the Julia package system.
Pkg.clone("https://github.com/kibaekkim/DSPsolver.jl");The following example shows a Julia script that uses DSPsolver.jl for solving a two-stage stochastic integer programming problem.
using DSPsolver, StochJuMP, MPI; # Load packages
# Initialize MPI
MPI.Init();
# random parameter
xi = [[7,7] [11,11] [13,13]];
# StochJuMP.jl model scripts
m = StochasticModel(3);
@defVar(m, 0 <= x[i=1:2] <= 5, Int);
@setObjective(m, Min, -1.5*x[1]-4*x[2]);
@second_stage m s begin
q = StochasticBlock(m, 1/3);
@defVal(q, y[j=1:4], Bin);
@setObjective(q, Min, -16*y[1]+19*y[2]+23*y[3]+28*y[4]);
@addConstraint(q, 2*y[1]+3*y[2]+4*y[3]+5*y[4]<=xi[1,s]-x[1]);
@addConstraint(q, 6*y[1]+1*y[2]+3*y[3]+2*y[4]<=xi[2,s]-x[2]);
end
DSPsolver.loadProblem(m); # Load model m to DSP
DSPsolver.solve(DSP_SOLVER_DD); # Solve problem using dual decomposition
# print out upper/lower bounds
println("Upper Bound: ", DSPsolver.getPrimalBound());
println("Lower Bound: ", DSPsolver.getDualBound());
# Finalize MPI
MPI.Finalize();
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