Modeling Airlift Operations for Humanitarian Aid and Disaster Relief to Support Acquisition Decision-Making

In a fiscally constrained environment, it is crucial that both equipment manufacturers and defence invest in technology that shows marked operational improvement. A priori identification of cost-benefit at the early acquisition stage is often limited and incomplete, leading to poor value propositions. This conundrum motivates the need to develop a method to evaluate technologies such as levels of autonomy, stealth capability, improved engines, etc. and make tradeoffs against operational measures of performance and effectiveness (MOP/Es) rather than solely against vehicle performance characteristics. The objective of this study is to create an environment in which those trades against MOEs could be performed rapidly to inform technology investment and acquisition decision-making. This environment is built on top of representative models of a discrete event simulation of disaster relief airlift operations to compare technology modifications or vehicle acquisition options rapidly against operational measures of effectiveness