OPTIMIZATION FOR HUMAN SYSTEMS INTEGRATION

Abstract

The stated objective of Human Systems Integration (HSI) in the DOD is optimization, specifically to maximize total system performance and minimize cost. Limited published precedents describe quantitative optimization across the HSI domain trade-spaces. The Model-Based HSI (MBHSI) process utilizes General Systems Performance Theory to relate HSI domain resource inputs to total system performance output. MBHSI also recharacterizes each domain in terms of constructs that are amenable to optimization via mathematical programs (MP). However, MBHSI does not provide an archetypal optimization model or method. This work pursues quantitative HSI optimization models and a method for creating those models in order to facilitate real-world tradespace decisions between the personnel, training, and human factors engineering domains. This work presents an MP formulation and solves it using an MBHSI data set and notional cost data. Sensitivity analyses further elucidate the trade space, and follow-on analyses demonstrate solution set changes based on varying resources, constraints, and cost parameters. Results indicate that MBHSI enables MP formulation and data set optimization. Decision makers are perpetually making trade-off decisions between HSI domains, many times without quantitative knowledge of the trade-space. MBHSI-derived MPs may enable responsive and evidence-based system change decisions in order to quantitatively achieve the stated goal of HSI— optimization.