Optimal satellite constellation spare strategy using multi-echelon inventory control with stochastic demand and lead times
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The recent growing trend to develop large-scale satellite constellations (i.e., mega-constellation) with low-cost small satellites has brought the need for an efficient and scalable maintenance strategy decision plan. Traditional spare strategies for satellite constellations cannot handle these mega-constellations due to their limited scalability in number of satellites and/or frequency of failures. In this paper, we propose a novel spare strategy using an inventory management approach. We consider a set of parking orbits at a lower altitude than the constellation for spare storage, and model satellite constellation spare strategy problem using a multi-echelon (s,Q)-type inventory policy, viewing Earth's ground as a supplier, parking orbits as warehouses, and in-plane spare stocks as retailers. This inventory model is unique in that the parking orbits (warehouses) drift away from the orbital planes over time due to orbital mechanics' effects, and the in-plane spare stocks (retailers) would receive the resupply from the closest (i.e., minimum waiting time) available warehouse at the time of delivery. The parking orbits (warehouses) are also resupplied from the ground (supplier) with stochastic lead time caused by the order processing and launch opportunities, leveraging the cost saving effects by launching many satellites in one rocket (i.e., batch launch discount). The proposed analytical model is validated against simulations using Latin Hypercube Sampling. Furthermore, based on the proposed model, an optimization formulation is introduced to identify the optimal spare strategy, comprising the parking orbits characteristics and all locations policies, to minimize the maintenance cost of the system given performance requirements. The proposed model and optimization method are applied to a real-world case study of satellite mega-constellation to demonstrate their value.
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