Built-in Flexibility for Space Logistics Mission Planning and Spacecraft Design

摘要

This paper develops a space logistics mission planning optimization framework considering uncertainty in space missions based on decision rules and stochastic programming. It makes space logistics mission planning and spacecraft design flexible to counter potential uncertainties in launch delays and staging delays. The rocket launch delay is considered as the uncertainty source. A scenario generation model is built to discretize the continuous launch delay probability distribution. Since a space mission may contain multiple rocket launches, a scenario reduction model is developed to recombine delay scenarios into a space mission uncertainty scenario. It can also decrease the number of scenarios to increase the computational efficiency of the mission planning framework. An example mission scenario based on Deep Space Gateway is considered. The results show that this optimization framework can provide a series of decision rules and spacecraft design, which can come up with a Pareto front between expected mission cost (i.e. initial mass in low-Earth orbit) and expected mission objective (i.e. effective crew time) under uncertainty environment The Pareto front plot and decision rules can help decision makers make decisions quickly when a launch delay happens in space mission. The scenario reduction method is also able to improve the computational efficiency significantly while maintaining an acceptable accuracy.