Emerging threats have focused U.S. Navy operating concepts on agile, distributed tactical forces in the littoral and maritime zones. Given the nature of the threat and its location, medical evacuation via air may be infeasible due to hostile conditions or distance, requiring a shift to a surface or subsurface strategy. Medical demand and adversary actions are unpredictable in warfare, therefore a decision process for routing that accounts for uncertainty is required. Using the principles of the U.S. Marine Corps Rapid Response Planning Process and online optimization, we propose a decision process for surface medical evacuation routing against an adversary given uncertain demand that can be applied to manned and autonomous transport operations. We showcase the computational tractability of the decision process by developing an algorithm to route a medical transport through a network then implement the algorithm as a simulation model in Python. The base case of the model is compared to two modified cases under perfect information to discuss the risks of modeling with inappropriate assumptions. Multiple runs of the simulation model are then used to propose a process to develop a distance multiplier to estimate the impact of adversary presence in existing simulation models without a complete re-design.Outstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited
