We are often faced with situations where we need to make decisions that have implications for personal and institutional goals. When there is uncertainty involved, we could either go with our gut feeling or take an analytical approach by characterizing the uncertainty, defining an objective, and evaluating the risk/payoffs of choices. This course is about the latter and is presented through the usage of example problem instances.INTENDED AUDIENCE : Any Interested LearnersPREREQUISITES : Undergraduate course in probability (including topics on random variables and expected value); calculus and algebra INDUSTRY SUPPORT : Most industries would find this useful (examples in course are in retail, supply chain and hospitality)
Overview
Syllabus
Week 1: Background and introduction: risk, uncertainty and variability; probability,random variables and expectation; optimization criteria; types of decisions Week 2: One-time decisions: secretary problem; utility function; decision trees;TV game shows; Monte Hall problem; project evaluation Week 3: Repeated decisions: newsvendor problem; buffering to manage uncertainty; safety stock for inventory; route planning; exploration vs. exploitation Week 4: Sequential adaptive decision-making: strategic and operational; stochasticprogramming; Simpson’s Paradox; Markov decision process
Taught by
Prof. N. Gautam
