This paper presents an integrated optimization framework for the planning, routing, and scheduling of many-to-many In-Orbit Servicing (IOS) operations in Low Earth Orbit (LEO). The purpose of this work is to overcome key limitations of existing IOS planning approaches, including deterministic assumptions, the separation of trajectory optimization from task sequencing, and the lack of integrated economic assessment. The objective is to develop a unified and scalable decision-support framework for the design and sustainable operation of future IOS infrastructures.

The methodology models the orbital environment as a dynamic, time-expanded logistics network in which customer satellites, servicers, and orbital depots evolve over discrete time steps. Orbital motion and maneuvering are represented through a tailored set of impulsive transfers for near-circular Sun-synchronous orbits, including multi-revolution phasing, coasting arcs, and J2-assisted cross-orbital transfers. Based on this network representation, IOS mission planning is formulated as a detailed Mixed-Integer Linear Programming (MILP) model that jointly optimizes trajectory selection, task sequencing, resource management, and depot-based resupply. The formulation captures key operational features of IOS missions, including service windows, fixed service durations, heterogeneous tools and consumables, collision avoidance constraints, and a comprehensive profit-maximizing objective function accounting for revenues, delay penalties, operating costs, launch costs, and purchase, development, and manufacturing costs.

To address uncertainty arising from unpredictable service needs, such as repairs and active debris removal, the optimization is embedded within a Rolling Horizon framework. Stochastic service requests are modeled as Poisson processes and revealed dynamically, requiring periodic re-optimization as new information becomes available. This approach enables adaptive and computationally tractable planning over extended horizons while preserving temporal and resource consistency across replanning cycles.

Results from operational case studies demonstrate that the framework generates feasible and efficient mission plans under dynamic demand conditions. Strategic case studies illustrate how the framework can be used to evaluate economic and operational trade-offs across alternative infrastructure architectures under varying market demand scenarios. In particular, two strategic analyses are conducted: one assessing the impact of varying depot configurations and another comparing alternative servicer fleet architectures, quantifying cumulative net profit, service revenue, delay costs, and resource utilization across low-, medium-, and high-demand conditions. Their findings enable the identification of economically viable and operationally robust IOS configurations under uncertainty. The framework thus supports both operational decision-making and long-term strategic planning of IOS infrastructures.