Schedule of Events
Presentations
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Can Deep Reinforcement Learning Improve Inventory Management? Performance on Dual Sourcing, Lost Sales and Multi-Echelon Problems.
Joren Gijsbrechts, Universidade Católica Portuguesa, Portugal -
Opportunities for Ethical ML and Supply Chains
Swati Gupta, Georgia Tech -
Heuristics for Mixed-Integer Optimization Through a Machine Learning Lens
Andrea Lodi, Cornell Tech, USA -
How to Make Sure Machine Learning Has an Impact on Supply Chains.
Polly Mitchell-Guthrie, Kinaxis -
From Small to Large Data – How Can We Leverage Synthetic Data for ML In Operations & Supply Chain Management.
Richard Pibernik, Julius-Maximilians-University, Würzburg, Germany -
Almost Matching Exactly for Observational Causal Inference.
Cynthia Rudin, Duke University, USA -
A Practical End-to-End Inventory Management Model with Deep Learning.
Zuo-Jun Max Shen, UC-Berkeley, USA / Univ. of Hong Kong -
Algorithmic Tools for US Congressional Districting: Fairness via Analytics
David B. Shmoys, Cornell University, NY, USA -
Chips and Beer: Using Machine Learning to Optimize Inventory
Larry Snyder, Lehigh University -
Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment In Warehouse Operations.
Jiankun Sun, Imperial College London, UK -
Data-driven Stochastic Vehicle Routing Problem and Job Shop Using Reinforcement Learning
Martin Takáč, Lehigh University, Bethlehem, USA -
Same-Day Delivery with Fair Customer Service
Barrett Thomas, University of Iowa, USA -
AI/ML Applications for Industry: The Proven and Future Direction
Michael Watson, Coupa Software
* Please note: subject to change due to ongoing uncertainities on travel restrictions/guidelines within the USA and internationally due to COVID.