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.
With the increasing integration of automation technologies, the role of the operator is changing from sole actor to a shared supervisor/actor role. Studies on unmanned ground vehicle operators and recent crashes partially blamed on automation technologies demonstrate the need to measure and assess operator awareness and workload. Overcoming these challenges requires an assessment early in the design cycle for operator awareness and workload. This methodology integrates concepts from cognitive engineering into operations analysis to better capture and analyze the effectiveness of increasingly automated systems. An agent-based model is created using Operational Event Sequence Diagrams and concepts from situation awareness research to guide agent formulation. The agent rule set is then mapped to the NASA Task Load Index scales to provide a dynamic output throughout the simulation. A traffic model is built in AFSIM to compare the mental workload associated with city versus highway driving. The dynamic workload measurement is the first step in a framework which will enable automation technologies to be traded during the conceptual design phase.
With the advent of new technologies for electric ships, there is a need for a robust methodology to quantitatively evaluate their impact on the performance of a ship, while accounting for the uncertain nature of their parameters. To that end, this paper gives an overview of the Technology Identification, Evaluation, and Selection, or TIES, methodology as applied a 10kton surface combatant. This case study highlights the ability of TIES to aid in a broad exploration of the design space, by giving designers key tools that allow them to show in a traceable manner the tradeoffs involved in infusing technologies and making other design choices, as well as which designs best meet different sets of Figures of Merit. This ultimately allows decision-makers to determine what technologies or design choices to invest in to yield a ship with the performance parameters that will best serve the needs of its stakeholders.
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