Optimal Munitions Mix for USMC Mobile Anti-Ship Missiles Launcher

NPS NRP Project PosterThe Marine Corps is developing anti-surface warfare (ASuW) capabilities as part of its Expeditionary Advanced Base Operations (EABO) concept. The purpose is to provide additional sea-control and sea-denial capabilities as part of an integrated naval ground-based anti-ship missile system (GBASM). A key question concerning GBASM is what is the best mix of munitions to be deployed for the sea-control and sea-denial mission. Besides the obvious fire-effectiveness criterion and its impact on defense capabilities, issues of mobility, vulnerability and logistics should be addressed too within a Pacific Theater scenario. Leveraging existing literature on firing theory and target assignment models, we will develop a descriptive probabilistic model, and associated measures of effectiveness (MOEs), for scenario-dependent evaluation of various munitions' mixes. The descriptive models will be the base for a prescriptive model that will optimize munition mixes and tactical employments. The output of this research will be a methodology for assessing the effect of munitions' mixes on defensive and counter-fire capabilities in the possible presence of other weapons, and a method for optimizing these mixes. The research questions are: What are the MOEs for evaluating munition-mixes in the GBASM context? How to utilize the MOEs to determine optimal in-context (South China Sea) munition-mixes at the tactical level? How to utilize insights obtained from the tactical level to determine deployed mixes for a campaign at the operational level? Based on the answers for questions (2) and (3), what would be the optimal mix of munitions at the strategic level? The research plan will follow a hierarchical bottom up pattern. We first formulate a set of reference tactical scenarios defined by (1) type of threat (2) combat intensity (3) ranges and (4) environmental conditions. Next we will develop a combat model (e.g., stochastic Lanchester) that will take as inputs Blue and Red munitions' characteristics (range dependent probabilities of kill, fire-rate, detection capabilities, etc.) and produce attrition values. In the third stage we will solve resource allocation problems that will optimize scenario-dependent munition mixes. Deliverables: Final report, IPRs, poster, exec summary, algorithmsHQMC Programs & Resources (P&R)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.