(Georgia Institute of Technology, 2022-01-14)
Gradini, Raffaele
Long-term naval planning has always been a challenge, but in recent years the difficulty has increased. The degradation of the security environment is leading toward a more volatile, uncertain, complex, and ambiguous world, heavily affecting the quality of predictions needed in long-term defense technology investments. This work tackles the problem from the perspective of the maritime domain, with a new approach stemming from the state-of-the-art in the defense investment field. Moving away from classic methodologies that rely on well-defined assumptions, it is possible to find investment processes that are broad enough, yet concrete, to support decision making in naval technology trades for science and technology purposes. In fulfilling this objective, this work is divided in two main areas: identifying technological gaps in the security scenario and providing robust technology investment strategies to cover those gaps. The core of the first part is the capability of decomposing maritime assets using modern taxonomies, to map the impact of different technologies on ships. Once technologies are mapped, they can be traded inside assets, and assets inside fleets to quantitatively evaluate the overall fleet robustness. The first deliverable achieved through this process is called Vulnerable Scenarios, a list of possible conflict scenarios in which a tested fleet would consistently fail. The second deliverable is called Robust Strategies and is made of different technological investments to allow the studied fleet in succeeding the discovered Vulnerable Scenario. To find the first deliverable a large set of scenarios were simulated. The results of this simulation were analyzed using the Patient Rule Induction Method to isolate, among the large set of relevant cases, a subgroup of Vulnerable Scenarios. These were identified by highlight commonalities on shared parameters and variables. Once the Vulnerable Scenarios were discovered, an ad-hoc adaptive response system using a “signpost and trigger” mechanism was used to identify different technologies on the ships studied that could enhance the overall robustness of the fleet. In identifying these technologies, the adaptive system was supported by different taxonomies in performing the different technological trades that allowed the algorithm to find Robust technology Strategies. The methodology was completed by a ranking system that was designed to firstly check all the Robust Strategies in all the scenarios of interest, and then to compare them against ranking metrics defined by decision makers.
To test the created methodology, several experiments were conducted across two use cases. The first use case, which involved an anti-submarine warfare (ASW) mission, was used to demonstrate the individual pieces employed in the creation of the methodology. The second use case, involving a large operation made of several tasks, was used to test the overall methodology as one. Both use cases were designed on the same original scenario created in collaboration with former generals and admirals of the US Air Force and the Italian Navy. The primary results of this experiments show that once Vulnerable Scenarios are discovered, it is possible to employ an iterative algorithm that recursively infuse new technologies into the fleet. This process is repeated until Robust Technology Strategies that can support the fleet are selected. The missions designed demonstrated the presence of gaps which had to be covered via technology investment showing how planners will have to account for new technologies to be able to succeed in future challenges.
The methodology created in this thesis provided an innovative way of enhancing the screening of maritime scenarios, reducing the leading time for investment decisions on naval technologies. In conclusion, the work done in this thesis helps in advancing the state of the art of methodologies used by planners when looking for Vulnerable Scenarios and for new technologies to invest on. Therefore, this thesis demonstrates that by employing the proposed methodology, Vulnerable Scenarios and relevant technologies can be identified in less time than by employing current methods. These efforts will support planners and decision makers in reacting faster to new emerging threats in unforeseen naval scenarios and, will enable them to identify in a rapid fashion in which areas more investments are needed.