Eojin Han

Welcome to my website! I'm a fifth year PhD student in Industrial Engineering and Management Sciences at Northwestern University, advised by Prof. Omid Nohadani and co-advised by Prof. Chaithanya Bandi. My research is broadly on sequential decision making under uncertainty. For these problems, information on the uncertainty is often limited. Using the tool from robust and distributionally robust optimization, I am working on designing efficient policies to provide approximate solutions. These problems have broad applications in operations management, such as healthcare, supply chains, and crowdsourcing.

Full CV is available upon request, and feel free to reach out to me via email: eojinhan2020 "at" u.northwestern.edu.

Education


Ph.D., Northwestern University (expected 2020)
Bachelor of Science, Seoul National University (2015)

Publications


  1. C. Bandi, E. Han and O. Nohadani. Sustainable Inventory with Robust Periodic-Affine Policies and Application to Medical Supply Chains. Management Science, 65(10):4636-4655 (2019). [Online link] [Featured article in Kellogg Insight]

    We introduce a new class of adaptive policies called periodic-affine policies, which allows a decision maker to optimally manage and control large-scale newsvendor networks in the presence of uncertain demand without distributional assumptions. These policies are data-driven and model many features of the demand such as correlation and remain robust to parameter misspecification. We present a model that can be generalized to multiproduct settings and extended to multiperiod problems. This is accomplished by modeling the uncertain demand via sets. In this way, it offers a natural framework to study competing policies such as base-stock, affine, and approximative approaches with respect to their profit, sensitivity to parameters and assumptions, and computational scalability. We show that the periodic-affine policies are sustainable — that is, time consistent — because they warrant optimality both within subperiods and over the entire planning horizon. This approach is tractable and free of distributional assumptions, and, hence, suited for real-world applications. We provide efficient algorithms to obtain the optimal periodic-affine policies and demonstrate their advantages on the sales data from one of India’s largest pharmacy retailers.

  2. C. Bandi, E. Han and O. Nohadani. On Finite Adaptability in Two-stage Distributionally Robust Optimization. Submitted.

  3. C. Bandi, E. Han and O. Nohadani. Towards Geometric Construction of Optimal Policies for Dynamic Optimization. Working paper.

Teaching Experience


Invited Presentations