Modeling Reconnaissance Squadron Workflow Using Discrete Event Simulation (DES) and Analyzing Several Measures of Effectiveness

Reconnaissance missions are not only one of the vital modes of intelligence-gathering methods, they are one of the most important contributors of military intelligence as well. They show the battlefield as it is to the commander. A simplified reconnaissance cycle includes the arrival of reconnaissance requests, planning of reconnaissance flights, flying the mission and exploitation of the films or images, and then dissemination of the intelligence reports. The reconnaissance cycle is modeled for four different scenarios (peace and war as situations, RF-4 and F-16 as configurations). There are two points of view regarding this cycle. The first is the reconnaissance requesters’ view: they want to know the estimated time it would take for a request to be answered, based on the resources and other factors, before an actual request was made. The second is the reconnaissance squadron commanders’ perspective: they want to respond to as many reconnaissance requests as possible. For that reason, they want to know and revise the ideal numbers of personnel and equipment. Analysis includes regression models and partition trees. When results are considered, we see that there is no common rule to determine which factors (either decision or noise) are the key determinants for each scenario. But we noticed that noise factors have much more impact on several measures of effectiveness than decision factors in each model.