IE Semineri: “Decision-Aware Learning for Global Health Supply Chains”, Hamsa Bastani, 16:00 30 Eylül (EN)

“Decision-Aware Learning for Global Health Supply Chains” by Hamsa Bastani, University of Pennsylvania
Speaker: Hamsa Bastani, University of Pennsylvania

Date & Time: September 30, 2022, Friday 16:00

***This is an online seminar. To obtain the event link, please send a message to department.

Title: Decision-Aware Learning for Global Health Supply Chains

Abstract: The combination of machine learning (for prediction) and
optimization (for decision-making) is increasingly used in practice.
However, a key challenge is the need to align the loss function used to
train the machine learning model with the decision loss associated with the
downstream optimization problem. Traditional solutions have limited
flexibility in the model architecture and/or scale poorly to large datasets.
We propose a principled decision-aware learning algorithm that uses a novel
Taylor expansion of the optimal decision loss to derive the machine learning
loss. Importantly, our approach only requires a simple re-weighting of the
training data, allowing it to flexibly and scalably be incorporated into
complex modern data science pipelines, yet producing sizable efficiency
gains. We apply our framework to optimize the distribution of essential
medicines in collaboration with policymakers at the Sierra Leone National
Medical Supplies Agency; highly uncertain demand and limited budgets
currently result in excessive unmet demand. We leverage random forests with
meta-learning to learn complex cross-correlations across facilities, and
apply our decision-aware learning approach to align the prediction loss with
the objective of minimizing unmet demand. Out-of-sample results demonstrate
that our end-to-end approach significantly reduces unmet demand across
1000+ health facilities throughout Sierra Leone. (Joint work with O.
Bastani, T.-H. Chung and V. Rostami).

Bio: Hamsa Bastani is an Assistant Professor of Operations, Information, and
Decisions at the Wharton School, University of Pennsylvania. Her research
focuses on developing novel machine learning algorithms for data-driven
decision-making, with applications to healthcare operations, social good,
and revenue management. Her work has received several recognitions,
including the Wagner Prize for Excellence in Practice (2021), the Pierskalla
Award for the best paper in healthcare (2016, 2019, 2021), the Behavioral OM
Best Paper Award (2021), as well as first place in the George Nicholson and
MSOM student paper competitions (2016).