Dynamic network modeling for spaceflight logistics with time-expanded networks

Abstract

This research develops a dynamic logistics network formulation for high-level lifecycle optimization of space mission sequences in order to find an optimal space transportation architecture considering its technology trades over time. The proposed methodology is inspired by terrestrial logistics analysis techniques based on linear programming network optimization. A new model with a generalized multi-commodity network flow formulation and a time-expanded network is developed for dynamic space logistics optimization. The developed methodology is applied to three case studies: 1) human exploration of Mars; 2) human exploration of a near-Earth object (NEO); 3) their combination (related to the concept of the Flexible Path). The results reveal multiple dynamic system-level trades over time and provide recommendations for an optimal strategy for human space exploration architecture. The considered trades include those between in-situ resource utilization (ISRU) and propulsion technologies as well as orbit and depot location selection over time. The numerical results show that using specific combinations of propulsion technologies, ISRU, and other space infrastructure elements effectively, we can reduce the initial mass in low- Earth orbit (IMLEO) by 45-50% compared with the baseline architecture. In addition, the analysis results also show that we can achieve 15-20% IMLEO reduction by designing Mars and NEO missions together as a campaign compared with designing them separately owing to their common space logistics infrastructure pre-deployment. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans, thus transforming us into a multi-planet species.