ROmodel: Modeling robust optimization problems in Pyomo

TL;DR

ROmodel is an open source Python package that extends Pyomo to facilitate modeling and solving robust optimization problems, supporting various uncertainty sets and decision rules, demonstrated through multiple case studies.

Contribution

It introduces ROmodel, a novel tool that simplifies the formulation of robust optimization models in Pyomo, including custom uncertainty sets and data-driven approaches.

Findings

Successfully applied to six case studies.

Supports custom and data-driven uncertainty sets.

Enables comparison of different reformulations.

Abstract

This paper introduces ROmodel, an open source Python package extending the modeling capabilities of the algebraic modeling language Pyomo to robust optimization problems. ROmodel helps practitioners transition from deterministic to robust optimization through modeling objects which allow formulating robust models in close analogy to their mathematical formulation. ROmodel contains a library of commonly used uncertainty sets which can be generated using their matrix representations, but it also allows users to define custom uncertainty sets using Pyomo constraints. ROmodel supports adjustable variables via linear decision rules. The resulting models can be solved using ROmodels solvers which implement both the robust reformulation and cutting plane approach. ROmodel is a platform to implement and compare custom uncertainty sets and reformulations. We demonstrate ROmodel's capabilities by applying it to six case studies. We implement custom uncertainty sets based on (warped) Gaussian processes to show how ROmodel can integrate data-driven models with optimization.