Event-Driven Space Logistics Network Optimization with Embedded Propulsion Technology Trades

Numerous high-thrust and low-thrust space propulsion technologies have been developed in the recent years with the goal of expanding space exploration capabilities; however, designing and optimizing a multi-mission campaign with both high-thrust and low-thrust propulsion options are generally computationally challenging due to the coupling between logistics mission design and trajectory evaluation. More specifically, this computational burden arises from the dependence of the $\Delta$v and time of flight for low-thrust trajectories on the payload mass, which in turn needs to be evaluated together with the logistics mission design. To tackle this challenge, this paper develops a novel event-driven space logistics network optimization approach using mixed-integer linear programming for campaign-level space mission design. The specific case of optimally designing a cislunar propellant resupply chain to support multiple lunar surface access missions is used to demonstrate this new space logistics framework. Surrogate models for trajectory evaluation are developed and integrated into mission design to enable internal trade-offs between available propulsion technology options. The results are compared with an existing stochastic combinatorial formulation developed for incorporating low-thrust propulsion into space logistics design. Our new approach provides superior results in terms of cost as well as utilization of the vehicle fleet. The developed event-driven space logistics network optimization method can trade off cost, time, and technology in an automated manner to optimally design space mission campaigns.