Optimization 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-ε)-optimal flexibility structures with only O(ln (1/ε)) overhead cost, an exponential improvement compared to the best known O(1/ε) 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’ choice model parameters. Combining combinatorial techniques and the powerful lower-upper confidence bound (LUCB) method, we develop data-driven algorithms to simultaneously learn consumers’ model and optimize assortment selection decisions. Our algorithms’ 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.
Yuan Zhou is currently an Assistant Professor at the Computer Science Department of Indiana University at Bloomington. Before joining Indiana University, he 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’s research interests include stochastic and combinatorial optimizations 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.