Abstract: 

While machine learning helps businesses with their data-driven decision-making, it cannot solve all business problems. In addition to predictive modeling, there are numerous situations in which prescriptive models are required, where the optimal decision is needed considering the business constraints. Data scientists may not learn about prescriptive analytics or encounter any problems that can be solved using optimization.

This workshop will introduce you to optimization as a powerful tool in your analytics toolbox. You'll learn what optimization is, how to think about a problem through an optimization lens, and how to formulate the problem. Having heard about supply chain in the past two years due to shipping delays and labor shortages, it's only fitting to discuss an optimization problem within this domain. You will see how to formulate that problem mathematically, how to add complexity to it step by step, and how to solve it utilizing Python and Gurobi, a leading commercial optimization solver.

Bio: 

Ehsan is a Principal Operations Research Scientist at Decision Spot, with knowledge in logistics and transportation industries. Over the years, he has worked with several Fortune 500 companies, including GE, Norfolk Southern, and C.H. Robinson. Ehsan has worked on a variety of supply chain projects and has focused primarily on network optimization and routing. Before joining Decision Spot, he worked at Opex Analytics, which was acquired by Llamasoft, and later by Coupa.

He holds a PhD in Industrial Engineering and has been an Adjunct Lecturer at Northwestern Master of Science in Analytics (MSiA) program since Fall 2019.