Machine Learning Seminar: Li Chen -- Managing Inventory and Pricing with Contextual Robust Optimization

The details about the machine learning seminar are as follows: 

Time: Mon 13 April (1:00 - 2:00pm): 

Location: SMRI Seminar Room (A12-03-301) A12 Macleay Building, Level 3, Room 301.  

Speaker: Li Chen (USYD) 

Title: Managing Inventory and Pricing with Contextual Robust Optimization 

Abstract: Multiproduct inventory and pricing problems are traditionally approached by
estimating a presumed "sufficiently accurate" demand model and then optimizing with it
to determine optimal inventory and pricing decisions.  However, obtaining an accurate
demand model is nearly impossible due to unobservable parameters, resulting in parameter
uncertainty; meanwhile, the unknown distribution of the error term in the stochastic
demand model introduces residual ambiguity.  Additionally, the predicted demand is
endogenously affected by pricing, leading to decision-dependent predictions that often
brings about intractable bilinear optimization problems.  We introduce a contextual
robust optimization model that addresses these challenges simultaneously.  Our proposed
model possesses attractive finite-sample performance guarantees and can be effectively
approached using an enhanced affine recourse adaptation to address intractability.  Our
framework can be readily extended to broader contextual decision-making problems under
mild conditions.  Extensive numerical studies demonstrate the effectiveness of our
approach, showing that it outperforms the conventional estimate-then-optimize approach
and the residual-based robust optimization approach that does not account for parameter
uncertainty, particularly when the available data is limited.  Notably, our proposed
model exhibits greater resilience when contextual information is disregarded, reflecting
practical situations in which collecting such information might be impossible or
costly.