Optimal Replenishment Strategy for Large Satellite Constellations

  • Ebrahim Almansoori

    Student thesis: Master's Thesis


    Large satellite constellations, or as they are known, satellite mega-constellations, are the trend now in the space industry. Many commercial companies have invested in those systems. There are many applications for mega constellations such as telecommunication services and global broadband services. The main advantage of mega-constellations over traditional constellations is that they tend to cost less money. However, mega-constellations face many challenges. One of the main challenges is the maintainability of the system. Traditional satellite constellations are less complex in terms of their maintenance due to the limited number of satellites in the system, while mega-constellations have hundreds to thousands of satellites. This research aims to develop replenishment strategies that can assure the maintainability and functionality of satellite mega-constellations. This research proposes an optimal replenishment strategy for satellite mega-constellations that is developed for the entire lifetime of the constellation. The strategy is developed based on lot-sizing and the periodic review (R, S) inventory policy. It mainly focuses on ground spares. Nowadays, launch events are widely available since many commercial entities provide rideshare launches. The model assumes that the launch timings are in control as the launch could be arranged within one year by the maximum. The demand calculations along with safety stock quantity rely on the constellation reliability function. Then, an optimization model is developed to relate all cost parameters associated with the sparing strategy in which it seeks to minimize the total replenishment cost. The results showed that utilizing the rideshare launch option will reduce the replenishment cost.
    Date of AwardMay 2021
    Original languageAmerican English


    • Satellite Mega-Constellations
    • Replenishment Strategy
    • Satellite Launch
    • Inventory Policies
    • and Linear Programming.

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