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U-M Industrial & Operations Engineering pres.

IOE 899 Seminar Series: He Wang, Georgia Tech

A Re-solving Heuristic with Uniformly Bounded Loss for Network Revenue Management

Photo of He Wang Photo of He Wang
Photo of He Wang
The IOE 899 Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4:00 pm-5:00 pm.

Title: A Re-solving Heuristic with Uniformly Bounded Loss for Network Revenue Management

We consider the classical Network Revenue Management problem, where a firm has limited resources and needs to irrevocably accept or reject customer requests in order to maximize expected revenue. We study a class of “re-solving heuristics” for this problem. These heuristics periodically re-optimize an approximation of the problem known as the deterministic linear program (DLP), where random customer arrivals are replaced by their expectations. We find that, in general, frequently re-solving the DLP produces the same order of revenue loss as one would get without re solving, which scales as the square root of the problem size. However, by re-solving the DLP at a few selected points in time, we design a new re-solving heuristic, whose revenue loss is bounded by a constant that is independent of the problem size.

(Joint work with PhD student Pornpawee Bumpensanti. Paper is available at:

He Wang is an Assistant Professor in the School of Industrial and Systems Engineering at Georgia Tech. His research interest is in revenue management, supply chain and logistics, and statistical learning. His recent research focuses on developing data-driven methods for the interface between machine learning and operations management. He received his Ph.D. in Operations Research and M.S. in Transportation at MIT, and his B.S. in Industrial Engineering and Math from Tsinghua University. His works have been awarded for Amazon Research Award (2018), INFORMS JFIG paper competition (1st place), IBM service science best student paper award (finalist), and CSAMSE best paper award (2nd place).

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