Optimizing the Allocation of Sensor Assets for the Unit of Action

摘要

The U.S. Army's Objective (Future) Force is being developed as a faster, lighter, more rapidly deployable alternative to the current force structure. The development of a strategy for the allocation of the Unit of Action's organic sensing assets is necessary to achieve the maximum situational awareness and information dominance required for successful combat operations on the future battlefield. This paper presents a methodology for finding an appropriate mix and allocation strategy for organic Unit of Action sensors in a given scenario. Specifically, the author developed a mathematical programming model to analyze the mix and allocation of organic sensor assets using an optimization-based approach. The model requires the following inputs: an inventory of sensors and platforms, a list of asset configurations known as packages, and an intelligence-based clustering of targets. The model then creates operationally feasible assignments of packages to target clusters, maximizing the weighted number of targets detected. Stochastic optimization and mixed integer linear programming were used to accomplish this goal. Two optimization models were developed: (1) a Sensor Mix Model that, given a fixed mix or inventory, allocates assets to target areas on the battlefield; and (2) a Sensor Mix Model that suggests an organic mix of sensors for consideration in developing the Objective Force structure. These models have the potential to be used as operational decision support tools for the unit commander. The notional data set used for model development included 10 platform types, 10 target clusters, 10 target categories, 4 enemy orders of battle, and 4 outcomes. However, these inputs could be easily modified based on the requirements of the user or analyst.