Related tools

	Input	Model changes needed	VSS / EVPI	Out-of-sample verdict	Checks of the lift
StochLift	PuLP, Pyomo, gurobipy, OR-Tools, highspy, .lp/.mps	none: the unchanged build_model(data)	both	paired test with bootstrap interval	8 solver-checked invariants
mpi-sppy	Pyomo; AMPL, GAMS, gurobipy guests (alpha); MPS + JSON; SMPS	a scenario_creator declaring the first stage	VSS (--vss)	MMW and bootstrap confidence intervals	configuration checks
StochasticPrograms.jl	Julia	rewrite with its macros	both	SAA confidence intervals	no
GAMS EMP SP, AIMMS, LINGO	their own languages	annotate random parameters and stages	LINGO: both	no	no

(As of October 2026; these projects move, so check their documentation.)

Use mpi-sppy for large models. It has decomposition (progressive hedging, Benders), parallel computing and a much larger set of algorithms. StochLift is meant for the step before that: finding out, from the model you already have, whether a stochastic model is worth building, and getting a lift you can trust. It can then export the scenarios to mpi-sppy. Many of its statistical tools (the Mak-Morton-Wood gap, CVaR) are standard and are also in mpi-sppy.

Language models. Recent work generates stochastic or robust models from text descriptions (for example DAOpt, arXiv:2511.11576, and arXiv:2508.17200). StochLift starts from model code instead, and a language model is optional: stochlift init --llm anthropic:<model id> drafts the spec, which is then validated against the real model and reviewed by a person.