All attendees receive free access to the INFORMS 2019 TutORials in Operations Research online content concurrently with the meeting. Registrants of the 2019 INFORMS Annual Meeting have online access to the 2019 chapters, written by select presenters, beginning on October 19, 2019. Access this content using the link provided to all attendees by email or, if you are a 2019 member, simply login to INFORMS PubsOnLine.

The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field, and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meetings.

Omni Channel Fulfillment Optimization

Author: Vivek Farias, MIT

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Authors: Daniel Kuhn, EPFL; Peyman Mohajerin Esfahani, Viet Anh Nguyen and Soroosh Shafieezadeh Abadeh

Computer Vision and Deep Learning

Authors: Param Singh, CMU; Nikhil Malik

The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis

Authors: Cynthia Rudin, Duke; David Carlson

Healthcare Analytics

Authors: Retsef Levi, MIT; Kyan Safavi, Martin Copenhaver

Reinforcement Learning for Sequential Decision Making

Authors: Shipra Agrawal, Columbia; Douglas Shier

Scheduling an Overloaded Multiclass Many-Server Queue with Impatient Customers

Authors: Amy Ward, Chicago; Amber Puha

Data-driven Methods for MDPs with Parametric Uncertainty

Authors: Shie Mannor, Technion; Huan Xu

The Use of Data Science for Supply Chain Transportation Management

Author: Sam Eldersveld, Amazon.com

Field Experiments

Authors: Bradley Staats, UNC; Maria Ibanez

Structural Econometric Models

Authors: Yong Tan, UW; Yan Huang, Param Vir Singh