Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling

2026-07-28

AI Summary

This work addresses the challenge of globally solving distributed constraint optimization problems (DCOPs) in large-scale decentralized satellite scheduling under stringent communication constraints. To overcome this, the authors propose a novel framework that integrates online learning with problem decomposition. The approach decouples the DCOP into a high-level task assignment meta-problem and local scheduling subproblems, coordinated through a feedback-driven iterative pricing mechanism. By innovatively combining modern online learning algorithms with potential game modeling, the method efficiently converges to an equilibrium solution. Evaluated on realistic satellite scheduling scenarios, the proposed framework achieves over 99% satisfaction rate for observation requests, substantially outperforming existing baselines, which attain only 87%.

Abstract

Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically. We address this challenge from two complementary directions. We revisit the connection between DCOPs and potential games, and adapt modern online learning algorithms for equilibrium finding to DCOPs. We show that these algorithms are competitive with representative incomplete DCOP algorithms. We then turn to decomposition frameworks for large-scale DCOPs, motivated by large-scale decentralized satellite scheduling. We propose a new framework that separates a DCOP into two interacting subproblems: a high-level meta-DCOP for task allocation, and independent local optimization problems for scheduling. To couple the two levels, we develop a novel iterative pricing method that updates the meta-level utilities using feedback from the local optimizers. Combining our online learning methods with our iterative pricing framework, we obtain near-optimal performance on real-world decentralized satellite scheduling problem instances, fulfilling over 99% of observation requests compared with 87% for state-of-the-art baselines.

Problem

• Distributed Constraint Optimization
• Large-Scale Satellite Scheduling
• Decentralized Decision Making
• Scalability

Innovation

• online learning
• iterative pricing
• distributed constraint optimization
• potential games
• satellite scheduling