GBOML: a structure-exploiting optimization modelling language in Python

Abstract

Mixed-Integer Linear Programs (MILPs) have many practical applications. Most modelling tools for MILPs fall in two broad categories. Indeed, tools such as algebraic modelling languages allow practitioners to compactly encode models using syntax close to mathematical notation but usually lack support for special structures, while other tools instead provide predefined components that can be easily assembled but modifying or adding new components is difficult. In this work, we present the inner workings of the Graph-Based Optimization Modelling Language (GBOML), an open-source modelling tool implemented in Python combining the strengths of both worlds. GBOML natively supports special structures that can be encoded by a hierarchical hypergraph, offers syntax close to mathematical notation and facilitates the modular construction and reuse of time-indexed models. We detail design choices enabling these features and show that they simplify problem encoding, lead to faster instance generation times and sometimes faster solve times. We benchmark the times taken by GBOML, JuMP, Plasmo, Pyomo and AMPL to generate instances of a structured MILP. We find that GBOML outperforms Plasmo and Pyomo, is tied with JuMP but is slower than AMPL. With parallel model generation, GBOML outperforms JuMP and closes the gap with AMPL. GBOML has the smallest memory footprint. 

Keywords:

• Optimization
• linear programming
• MILP
• graph
• structure
• exploitation
• modelling
• tool

Acknowledgments

The authors would like to thank Hatim Djelassi for his work on a previous version of GBOML, his feedback and overall guidance and Benoit Legat for taking the time to answer questions about the inner workings of JuMP.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://jump.dev/JuMP.jl/stable/

2 https://docs.julialang.org/en/v1/manual/performance-tips/#Measure-performance-with-[@time](@ref)-and-pay-attention-to-memory-allocation

Additional information

Funding

The authors gratefully acknowledge the support of the Federal Government of Belgium through its Energy Transition Fund and the INTEGRATION project.