AI & ML
Weighted Fairness in Multi-User Earth Observation Satellite Scheduling
arXiv cs.MA0 views
arXiv:2610.34282v1 Announce Type: cross Abstract: As constellations of satellite grow in size, and become more costly, they are increasingly funded by multiple stakeholders who expect access to the constellation's resources that is commensurate with their investment. Such cooperation raises significant challenges for constellation operation, in particular regarding the fairness of the computed schedules among stakeholders. While several existing works deal with fairness in the Earth Observation Satellite Scheduling Problem (EOSSP), most of them assume that users (e.g. space agencies, research labs, countries) have the same weights, i.e. contributed equally to the project and therefore have equal access to the constellation. However, in practice, users' contributions often differ significantly. Hence, in this paper, we study a weighted variant of the EOSSP where users are assigned weights reflecting their respective contributions to the constellation. We propose several solution concepts with varying focus on efficiency and fairness, namely weighted utilitarianism, weighted egalitarianism, weighted leximin, and weighted ordered weighed averaging (WOWA). We formulate the related optimization problems as Mixed-Integer Linear Programs (MILPs), and compare them against two natural heuristics: weighted greedy and weighted round robin. All methods are evaluated on scenarios drawn from the literature, under strict runtime constraints, in order to assess the performance of each approach with respect to both efficiency and fairness metrics.
Source:https://arxiv.org/abs/2610.34282PreviousBuild, Don't Play: Self-Improving LLM Research Agents Develop a Hearthstone Battlegrounds AI That Rivals Top PlayersNext6G Reasoning Agent Societies: Composition, Scaling, and Consistency-Time Laws
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