A JuMP extension for Stochastic Dual Dynamic Programming

Julia package for formulating and analyzing stochastic recourse models.

DecisionProgramming.jl is a Julia package for solving multi-stage decision problems under uncertainty, modeled using influence diagrams. Internally, it relies on mathematical optimization. Decision models can be embedded within other optimization models.

Data-driven decision making under uncertainty using matrices

Benders decomposition to solve mixed integer linear programming, especially stochastic programming in seconds!

Implementation of the (dynamic) stochastic dual dynamic integer programming (SDDiP) algorithm.

Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.

Julia modeling interface to parallel decomposition solver DSP

Input data and source code for the paper "Modelling uncertainty in coupled electricity and gas system models—is it worth the effort?"

Solution of a two-stage stochastic model useful for investment planning using pyomo and mpi-sppy.

Implement integer-L-shaped method for solving two-stage stochastic programming

02435 - Decision-Making under Uncertainty

Devising an optimal portfolio choosing strategy based on stochastic programming

reading SMPS format files in C++ (Two-stage stochastic programs with fixed recourse)

Codebase for "A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs"

GNU MathProg EDSL for Scala with support for scenario-based multistage stochastic programming

Optimizing Costs for Cloud Computing with Stochastic Programming

Stochastic Optimization and ARIMA Model Forecast in Julia to Manage Airline Fleet

Stochastic linear program for investments in the European power system. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001424

VoteEnsemble: Ensemble methods for machine/deep learning and stochastic programming with guaranteed generalization.