Designing a Framework for Solving Multiobjective Simulation Optimization Problems

TL;DR

This paper surveys current MOSO algorithms and environments, discusses key challenges in designing a flexible framework, and demonstrates how to quickly develop customized solvers for real-world problems.

Contribution

It introduces the ParMOO framework, addressing integration challenges of diverse MOSO algorithms and enabling rapid deployment for real-world applications.

Findings

ParMOO effectively integrates multiple MOSO algorithms.

Customized ParMOO solvers solve real-world problems efficiently.

Framework addresses complexity and diversity in MOSO environments.

Abstract

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. While an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time consuming to integrate these techniques into real world production-ready solvers. This is partly due to the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly due to the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate modeling techniques, problem formulations, design spaces, and data acquisition functions. This paper carefully surveys the current state-of-the-art in MOSO algorithms, techniques, and solvers; as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a Parallel Multiobjective Simulation Optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems.