Flow-Based Task Assignment for Large-Scale Online Multi-Agent Pickup and Delivery
Yue Zhang, Zhe Chen, Daniel Harabor, Pierre Le Bodic, Peter J. Stuckey

TL;DR
This paper introduces a flow-based approach to online multi-agent pickup and delivery, enabling scalable, real-time task assignment for thousands of agents and tasks with improved efficiency and quality.
Contribution
It formulates the task assignment as a minimum-cost flow problem, supporting scalable, real-time multi-agent task assignment with congestion-aware cost models.
Findings
Supports over 20,000 agents and 30,000 tasks within 1 second.
Outperforms existing methods in efficiency and assignment quality.
Supports real-time execution with congestion-aware routing.
Abstract
We study the problem of online Multi-Agent Pickup and Delivery (MAPD), where a team of agents must repeatedly serve dynamically appearing tasks on a shared map. Existing online methods either rely on simple heuristics, which result in poor decisions, or employ complex reasoning, which suffers from limited scalability under real-time constraints. In this work, we focus on the task assignment subproblem and formulate it as a minimum-cost flow over the environment graph. This eliminates the need for pairwise distance computations and allows agents to be simultaneously assigned to tasks and routed toward them. The resulting flow network also supports efficient guide path extraction to integrate with the planner and accelerates planning under real-time constraints. To improve solution quality, we introduce two congestion-aware edge cost models that incorporate real-time traffic estimates.…
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Taxonomy
TopicsPeer-to-Peer Network Technologies · Mobile Agent-Based Network Management · Optimization and Search Problems
