ISyE Seminar Series: PhD Student Showcase
PhD Student Showcase
Yishun Luo
ISyE PhD student
"Stability and Heavy-Traffic Delay Optimality of General Load Balancing Policies in Heterogeneous Service Systems"
About the Seminar:
We consider a load balancing system consisting of n single-server queues working in parallel, with heterogeneous service rates. Jobs arrive to a central dispatcher, which has to dispatch them to one of the queues immediately upon arrival. For this setting, we consider a broad family of policies where the dispatcher can only access the queue lengths sporadically, every T units of time. We assume that the dispatching decisions are made based only on the order of the scaled queue lengths at the last time that the queues were accessed, and on the processing rate of each server. For these general policies, we provide easily verifiable necessary and sufficient conditions for the stability of the system, and sufficient conditions for heavy-traffic delay optimality. We also show that, in heavy-traffic, the queue length converges in distribution to a scaled deterministic vector, where the scaling factor is an exponential random variable.
Related Paper:
About the Speaker:
Yishun Luo is currently a second-year PhD student in the ISyE Department. His research focuses on queueing theory and its real-world applications. Before joining the PhD program, he was a master’s student in ISyE.
Chengwenjian Wang
ISyE PhD student
"Balancing Adaptability and Predictability: Limited Revision Multistage Stochastic Programming"
About the Seminar:
A standard assumption in multistage stochastic programming is that decisions are made after observing the uncertainty from the prior stage. The resulting solutions can be difficult to implement in practice, as they leave practitioners ill-prepared for future stages. To provide better foresight, we introduce the K-revision approach. This new framework requires plans to be specified in advance. To maintain flexibility, we allow plans to be revised a maximum of K times as new information becomes available. We analyze the complexity of K-revision problems, showing NP-hardness even in a simple setting. We develop two MIP formulations, one directly from our definition and the other based on a combinatorial characterization. We analyze the tightness of these formulations and propose several methods to strengthen them. Computational experiments on synthetic problems and practical applications demonstrate that our approach is both computationally tractable and effective in reaching near-optimal performance while increasing the predictability of the solutions produced.
Related Paper:
"Balancing Adaptability and Predictability: Limited Revision Multistage Stochastic Programming"
About the Speaker:
Chengwenjian Wang is a third-year Ph.D. student at UMN ISyE working with Prof. JP Richard. He is interested in discrete optimization and its real-world applications, with a particular focus on graphical decision models, production planning, and air traffic management. Before coming to UMN ISyE, he got a master's degree at ETH Zurich, where he implemented exam scheduling algorithms for the university. Before that, he got a bachelor's degree from Fudan University. Before that, he was born and raised in Hefei, China.
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