Optimal Decision Making Under Uncertainty
Description
Inventory control problems in supply chains. In this stream of theoretical research, Professor Goh has investigated how inventory should be optimally managed in supply chains. Specifically, he has studied how supply chains can make decisions to operate optimally when they experience random demand over multiple time periods, and when supply chain members have added options to make short-term order commitments in advance of their actual orders.
Theory and applications of distributionally robust optimization. Distributionally robust optimization is a modeling paradigm for decision making under uncertainty where optimal decisions or decision rules (for sequential problems) are sought for the worst-case distribution of uncertainty within a family. This modeling paradigm has the advantage that it often entails only modest data and computational requirements, even for complex problems. Professor Goh has studied how flexible nonlinear decision rules can be used for decision making within this paradigm, and has applied these ideas to the management of projects and financial portfolios. He has also created a freely distributed software package, ROME (Robust Optimization Made Easy), for modeling and solving such problems.