Embedded Files
Scheduling in Queueing Systems with Uncertain and Evolving Holding CostsÂ
Scheduling in Queueing Systems with Uncertain and Evolving Holding CostsÂ
Date: September 14, 2026
Speaker:
Wentao Weng
Wentao Weng
University of Michigan
More Info:
More Info:
Abstract
Abstract
In content moderation for social media platforms, the cost of delaying the review of a content is proportional to its view trajectory, which fluctuates and is apriori unknown. Motivated by such uncertain and evolving holding costs, we consider a queueing model where job states evolve based on a Markov chain with state-dependent instantaneous holding costs. We demonstrate that in the presence of such uncertain and evolving holding costs, the two canonical algorithmic principles, instantaneous-cost (cμ-rule) and expected-remaining-cost (cμ/θ-rule), are suboptimal. By viewing each job as a Markovian ski-rental problem, we develop a new index-based algorithm, Opportunity-adjusted Remaining Cost (OaRC), that adjusts to the opportunity of serving jobs in the future when uncertainty partly resolves. We show that the suboptimality gap of OaRC scales as \tilde{O}(\sqrt{N}), where N is the system size. This bound shows that OaRC achieves asymptotic optimality for overloaded systems when the system size N scales to infinity. Moreover, the bound is independent of the state-space size, which is a desirable property when job states contain contextual information. We corroborate our results with an extensive simulation study based on two holding cost patterns (online ads and user-generated content) that arise in content moderation for social media platforms. Our simulations based on synthetic and real datasets demonstrate that OaRC consistently outperforms existing practice, which is based on the two canonical algorithmic principles.Â
Speaker's Bio
Speaker's Bio
Wentao Weng is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. His research focuses on designing data-driven algorithms for stochastic systems and understanding the societal implications of such algorithms. His work has been recognized with the Public Sector OR Best Paper Award (2nd place, 2024), the Service Science Best Paper in Socially Responsible Research Award (1st place), the Junior Faculty Interest Group Paper Competition (finalist, 2024), and the Applied Probability Society Best Student Paper Competition (finalist, 2022). He obtained a PhD in electrical engineering and computer science from MIT and a bachelor’s degree in computer science from Tsinghua University.Â
Date: September 28, 2026
Speaker:
Linwei Xin
Linwei Xin
Cornell University
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Date: October 5, 2026
Speaker:
Rachel Cummings
Rachel Cummings
Columbia University
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Date: October 19, 2026
Speaker:
Sean Sinclair
Sean Sinclair
Northwestern University
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Date: November 9, 2026
Speaker:
Rouba Ibrahim
Rouba Ibrahim
University College London
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Date: November 16, 2026
Speaker:
Arnaud Doucet
Arnaud Doucet
University of Oxford
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Date: November 30, 2026
Speaker:
Flore Sentenac
Flore Sentenac
HEC Paris
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Date: December 7, 2026
Speaker:
Kuang Xu
Kuang Xu
Stanford University
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Date: January 25, 2027
Speaker:
Alexander Stolyar
Alexander Stolyar
University of Illinois at Urbana-Champaign
More Info:
More Info:
Abstract
Abstract
TBD
Speaker's Bio
Speaker's Bio
TBD
Google Sites
Report abuse