題目：Adaptive Distributionally Robust Linear Optimization with Applications in Vehicle Sharing

報(bào)告人：Dr. Meilin Zhang (Singapore University of Social Science)

時(shí)間：2018年12月24日（星期一）10：00—12:00

地點(diǎn)：bwin必贏唯一官網(wǎng)313室

Abstract:

Addressing uncertainty in manyreal-world optimization problems has often lead to computationally intractable models. As a result, uncertainty is often ignored in optimization models, and this may lead to poor or even unacceptable decisions when implementing them in practice.

We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity setof probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the uncertain outcomes as they unfold in stages.

To demonstrate the potential for solving management decision problems, we develop an algebraic modelling package and illustrate how it can be used to facilitate modelling and obtain high-quality solutions for vehicle sharing problem with repositioning.

Biography:

Dr. Meilin Zhang is a lecturer in the Analytics Program of Singapore University of Social Science, where she has been teaching quantitative methods, data analytics to management students since 2017. Meilin received her B.S. and M.S. degree in Computer Science from Chinaand her Ph.D. in Decision Science from NUS Business School in 2015. He rresearch is in the areas of robust optimization, healthcare analytics,large-scale computation. She has also published her work in leading management journals: Management Science, Operations Research and MSOM