銆愪富璁层€慪uan Zhou锛屽嵃绗畨绾冲ぇ瀛︿集鏄庨】鍒嗘牎鍔╃悊鏁欐巿
銆愪富棰樸€戦殢鏈轰紭鍖栧湪绠＄悊绉戝涓殑搴旂敤
銆愭椂闂淬€2018骞6鏈14鏃ワ紙鍛ㄥ洓锛11:30-13:00
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銆愯瑷€銆戜腑鑻辨枃
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銆愮畝鍘嗐€慪uan Zhou鑰佸笀鐨勭畝鍘
銆怱peaker銆慪uan Zhou, Assistant Professor, Indiana University at Bloomington
銆怲opic銆慡tochastic Optimization with Applications to Management
銆怲ime銆慣hursday, June 14, 11:30-13:00
銆怴enue銆慠oom 501, Weilun Building, Tsinghua SEM
銆怢anguage銆慍hinese and English
銆怬rganizer銆慏epartment of Management Science and Engineering
銆怉bstract銆慜ptimization with large-scale data has been playing an increasingly important role in operations management applications. In this talk, I will use the following two examples to demonstrate how to incorporate stochastic, combinatorial, and machine learning methodologies to address the emerging challenges in this direction.
1. Process flexibility is a critical operational strategy for manufacturing firms to improve their ability to match supply with random external demand. Using random graph theory, we show construction of (1-蔚)-optimal flexibility structures with only O(ln (1/蔚)) overhead cost, an exponential improvement compared to the best
known O(1/蔚) cost achieved by the popular chaining system.
2. Dynamic assortment planning concerns about the optimal displaying strategy to maximize total revenue over the selling season with no a priori information on consumers鈥 choice model parameters. Combining combinatorial techniques and the powerful lower-upper confidence bound (LUCB) method, we develop data-driven algorithms to simultaneously learn consumers鈥 model and optimize assortment selection decisions. Our algorithms鈥 performance guarantees surprisingly do not depend on the number of candidate products, which is particularly useful in settings such as fast fashion retailing and online advertising.
銆怋io銆慏r. Yuan Zhou is currently an Assistant Professor at the Computer Science Department of Indiana University at Bloomington. He is also a Visiting Assistant Professor at Shanghai University of Finance and Economics and an Adjunct Assistant Professor at University of Illinois at Urbana-Champaign. Before joining Indiana University, Yuan was an Applied Mathematics Instructor at the Mathematics Department of Massachusetts Institute of Technology. Prior to MIT, Yuan was the recipient of the Simons Graduate Fellowship and obtained his Ph.D. in Computer Science at Carnegie Mellon University. He also ranked the 1st in the International Olympiad in Informatics and the 2nd in the World Finals of ACM International Collegiate Programming Contest.
Yuan鈥檚 research interests include stochastic and combinatorial optimization and their applications to operations management and machine learning. He is also interested in and publishes on analysis of mathematical programming, approximation algorithms, and hardness of approximation.
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