A Taxonomy of Constraints in Simulation-Based Optimization

TL;DR

This paper introduces QRAK, a comprehensive taxonomy of constraints specific to simulation-based optimization, aiding modeling, problem formulation, and software development by clarifying different constraint types.

Contribution

The paper presents a formal taxonomy of constraints (QRAK) tailored for simulation-based optimization, addressing a gap in existing nonlinear programming constraint classifications.

Findings

Defines formal classes of constraints for simulation-based optimization

Provides illustrative examples for each constraint class

Facilitates better modeling and software development in simulation optimization

Abstract

The types of constraints encountered in black-box and simulation-based optimization problems differ significantly from those treated in nonlinear programming. We introduce a characterization of constraints to address this situation. We provide formal definitions for several constraint classes and present illustrative examples in the context of the resulting taxonomy. This taxonomy, denoted QRAK, is useful for modeling and problem formulation, as well as optimization software development and deployment. It can also be used as the basis for a dialog with practitioners in moving problems to increasingly solvable branches of optimization.