Optimal Scheduling in a Question-Answering Forum of Knowledge Workers

Computer Science > Artificial Intelligence [Submitted on 18 Jun 2026] Optimal Scheduling in a Question-Answering Forum of Knowledge Workers Rohit Negi, Mustafa Yilmaz As individuals turn to the Internet to find answers to questions they may have, several Question Answering (QA) forums have evolved, where users knowledgeable in certain topics can contribute their expertise to answering these requests for information. While these are currently volunteer based, we consider a future version employing knowledge workers who are experts in certain topics. In such a system, the request-answer processes forming the queuing system may utilize schedulers that assign requests in different topics to the experts in the forum, who may be able to answer them according to their expertise levels in different topics. With this model, we calculate the capacity of the system for handling the requests while keeping the system stable, and design schedulers that achieve capacity. We also investigate how collaboration between experts in answering requests can potentially increase capacity. Comments: 14 pages, 4 figures Subjects: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI) Cite as: arXiv:2606.19759 [cs.AI]   (or arXiv:2606.19759v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.19759 Focus to learn more Submission history From: Rohit Negi [view email] [v1] Thu, 18 Jun 2026 03:42:35 UTC (605 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.SI References & Citations NASA ADS 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)