10/5/23 | 4:15pm | E51-335
Tang Professor of Operations
Abstract: We analyze an inventory system where a set of items share a limited storage capacity or inventory budget in each of T periods of the planning horizon. Demands for the different items follow a general multivariate Normal distribution allowing for arbitrary correlation structures. Inventories may be adjusted by placing orders which arrive after a given lead time, or by salvaging part of the inventory. The constraints come in the form of expected value and chance constraints on the value of certain performance measures in each period. We design a heuristic which is asymptotically optimal as the number of different items goes to infinity; however, the demands are correlated only among items within a common product line.
Bio: Garud Iyengar is the Tang Professor of Operations at Columbia Engineering. He received his B. Tech. in Electrical Engineering from IIT Kanpur, and an MS and PhD in Electrical Engineering from Stanford University. His research interests are broadly in control, machine learning and optimization. His current projects focus on the areas of large-scale power systems and supply chains, causal inference, and modeling of cellular processes. He was elected an INFORMS Fellow in 2018. He was the Chair of the Department of Industrial Engineering and Operations Research from 2013-19, and the Associate Director for Research at the Columbia Data Science Institute from 2017-19. He has been an Amazon Scholar since 2019. He is currently the Senior Vice Dean for Research and Academic Programs at Columbia Engineering.