Operational Reliability of Deadline-Constrained Task Assignment: Stability Characterization and Adversarial Routing

[2511.05715] Operational Reliability of Deadline-Constrained Task Assignment: Stability Characterization and Adversarial Routing Skip to main content Search Submit Donate Log in Computer Science > Multiagent Systems arXiv:2511.05715 (cs) [Submitted on 7 Nov 2025 ( v1 ), last revised 25 Aug 2026 (this version, v4)] Title: Operational Reliability of Deadline-Constrained Task Assignment: Stability Characterization and Adversarial Routing Authors: Roee M. Francos , Daniel Garces , Orhan Eren Akgün , Stephanie Gil View PDF HTML (experimental) Abstract: Automated task-assignment systems often serve stochastic tasks subject to finite deadlines. In these settings, conventional backlog-based stability can be misleading: finite task lifetimes may keep the number of outstanding tasks bounded even as deadline failures continue indefinitely, while average failure-rate criteria can still permit recurrent failures. We introduce average cost stability, a criterion which is particularly useful for time-sensitive tasks, combining two observable quantities: the number of outstanding tasks and the cumulative number of irrecoverable deadline failures. Under bounded arrivals and uniformly bounded service windows, we show that the outstanding-task count is uniformly bounded independently of the assignment policy and prove that our average cost stability is equivalent to bounded expected cumulative failures. We further characterize degenerate backlog stability, in which backlog remains bounded despite unbounded cumulative failures. We instantiate the framework in an adversarial pickup-and-delivery system where internal fleet agents spoof reported locations to attract assignments and leave requests unserviced. We develop deadline-aware assignment procedures and adversarial models with varying knowledge and coordination capabilities. Experiments using real mobility-on-demand request data and our proposed adversarial models demonstrate that backlog can remain bounded while cancellations persist, whereas our proposed average cost stability correctly identifies such behavior as unstable. Comments: 23 pages, 5 figures Subjects: Multiagent Systems (cs.MA) ; Systems and Control (eess.SY) Cite as: arXiv:2511.05715 [cs.MA] (or arXiv:2511.05715v4 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2511.05715 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Roee Mordechai Francos [ view email ] [v1] Fri, 7 Nov 2025 21:18:14 UTC (275 KB) [v2] Fri, 19 Dec 2025 19:27:02 UTC (1 KB) (withdrawn) [v3] Sat, 27 Jun 2026 20:30:39 UTC (4,038 KB) [v4] Tue, 25 Aug 2026 00:24:49 UTC (6,744 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.MA < prev | next > new | recent | 2025-11 Change to browse by: cs cs.SY eess eess.SY References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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