# CAREER: Simulation Optimization Reimagined: Coupling Exploratory Simulation Analysis and Optimization for Holistic Decision Making

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · Texas A&M Engineering Experiment Station (TX) · $551,703

## Abstract

This Faculty Early Career Development Program (CAREER) grant will advance the national prosperity and economic welfare by enhancing the analytical capabilities of organizations in sectors such as healthcare, finance, construction, and national defense that leverage stochastic computer simulation models to make critical decisions in the face of uncertainty. This award supports a fundamental reinvention of how such models are paired with optimization methods to inform decision makers of risks and tradeoffs in stochastic system performance. This research will make simulation optimization approaches more systematic, productive, and aligned with user needs and facilitate more holistic decision making than conventional approaches. Close collaboration with industry partners will ensure the methods created are intuitive, informative, and practicable. The educational component of the project will create high school outreach activities and teaching modules that explore analysis techniques for simulation data and improve programming proficiency and statistical literacy. This project will also produce software, including open-source implementations of the methods, a prototype of an interactive dashboard, add-ins for commercial simulation software, and versions that are compatible with an open-source simulation optimization testbed used by researchers and educators.

The research is motivated by shortcomings of existing simulation optimization (SO) approaches, which generally require decision makers to specify summary performance measures to serve as objectives or constraints in an optimization problem. By beginning with a narrow problem formulation, SO practitioners often fail to think about their simulation model in the broadest stochastic sense. This research shifts the initial focus of SO from summary performance measures to distributions of performance measures, exposing the user to inherent risks and tradeoffs. The research invents a transformative framework that couples

## Key facts

- **NSF award ID:** 2543469
- **Awardee organization:** Texas A&M Engineering Experiment Station (TX)
- **SAM.gov UEI:** QD1MX6N5YTN4
- **PI:** David J Eckman
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** SIMULATION MODELS, CAREER-Faculty Erly Career Dev, OPERATIONS RESEARCH
- **Estimated total:** $551,703
- **Funds obligated:** $551,703
- **Transaction type:** Standard Grant
- **Period:** 07/01/2026 → 06/30/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2543469

## Citation

> US National Science Foundation, Award 2543469, CAREER: Simulation Optimization Reimagined: Coupling Exploratory Simulation Analysis and Optimization for Holistic Decision Making. Retrieved via AI Analytics 2026-07-05 from https://api.ai-analytics.org/grant/nsf/2543469. Licensed CC0.

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