🤖 AI Summary
This work addresses the challenge of cascading conflicts in multi-agent cooperative scheduling, where delays of individual agents can propagate and disrupt global coordination. To mitigate this, the authors introduce the concept of temporal flexibility, which quantifies the maximum delay each agent can tolerate without violating the global schedule order. Building on this notion, they propose the FlexSIPP algorithm, which precomputes feasible alternative paths for potentially affected agents and dynamically exploits temporal slack through time-dependent search to enable efficient, cascade-free replanning. Empirical evaluation on the Dutch railway network demonstrates that the method generates robust, operationally compliant schedules within practical time limits, significantly enhancing the system’s resilience to disturbances.
📝 Abstract
Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to quickly find a new safe plan. Replanning only the delayed agent often does not result in an efficient plan, and sometimes cannot even yield a feasible plan. On the other hand, replanning other agents may lead to a cascade of changes and delays. We show how to efficiently replan by tracking and using the temporal flexibility of other agents while avoiding cascading delays. This flexibility is the maximum delay an agent can take without changing the order of or further delaying more agents. Our algorithm, FlexSIPP, precomputes all possible plans for the delayed agent, also returning the changes for the other agents, for any single-agent delay within the given scenario. We demonstrate our method in a real-world case study of replanning trains in the densely-used Dutch railway network. Our experiments show that FlexSIPP provides effective solutions, relevant to real-world adjustments, and within a reasonable timeframe.