Concurrent Trajectory and Vehicle Optimization: A Case Study of Earth-Moon Supply Chain Logistics

The objective of this paper is to demonstrate an integrated system design optimization approach for space system networks. The decisions made during the initial design phases for a complex system, will drastically afiect the flnal product at the architectural level. Traditionally, the design process for a complex system involves the sequential design of the sub-system components, which may lead to a sub-optimal system design. In systems with a high degree of sub-system coupling, the ordering of the sub-system design decisions indirectly determines the priority of the sub-systems to the system. In addition, with the announcement of the space exploration initiative, the goal is to design a sustainable space exploration system that can accomplish multiple missions in an environment of uncertainty. As such, we can no longer consider each mission separately, and therefore must integrate the multiple missions into the initial design of the system. By viewing the set of missions as an integrated space network, we add another sub-system to the system design space. A systems level solution to this problem requires that the network, the vehicle, and the trajectory design, be considered concurrently. To illustrate a concurrent design optimization methodology, this paper considers the design of an Earth-Moon supply chain. Speciflcally, we consider the problem of delivering cargo units of water from low Earth orbit to lunar orbit and the lunar surface. The formulation requires that the architectural characteristics of the vehicle used to transport the packages to the destinations and the paths the vehicles travel be determined concurrently. The problem is solved using both traditional design optimization methods and a concurrent design optimization method. The vehicle optimization and concurrent optimization both achieve a minimum system mass of 58,768 kg for a single vehicle design, which is an improvement of 114,070 kg from the network optimization solution. The system objective is further reduced to 47,498 kg when multiple vehicles are designed. Initial investigations into the sensitivity of the solution to changes in demand reveal that the minimum system mass is obtained when direct routes to the demands nodes are travelled.