[Ml-stat-talks] Fwd: [ORFE-Seminars] TODAY: Department Colloquium: 4:30 Sherrerd Hall 101: David Brown, Duke University

Warren Powell powell at princeton.edu
Tue Dec 5 13:20:36 EST 2017

Talk of interest today in Sherrerd at 4:30, Room 101.


*===== TODAY: ORFE Department Colloquium Announcement=====*

DATE:             Tuesday, December 5, 2017

TIME:             4:30pm

LOCATION:   Sherrerd Hall 101

SPEAKER:      David Brown, Duke University

Title:               *Index Policies and Performance Bounds for Dynamic
Selection Problems*

*Abstract:        *We consider dynamic selection problems, where a decision
maker repeatedly selects a set of items from a larger collection of
available items. A classic example is the dynamic assortment problem, where
a retailer chooses items to offer for sale subject to a display space
constraint. The retailer may adjust the chosen assortment over time in
response to the observed demand. These dynamic selection problems are
naturally formulated as stochastic dynamic programs (DPs) but are difficult
to solve because optimal selection decisions depend on the states of all
items. In this paper, we study heuristic policies for dynamic selection
problems and provide upper bounds on the performance of an optimal policy
that can be used to assess the performance of a heuristic policy. The
policies and bounds that we consider are based on a Lagrangian relaxation
of the DP that relaxes the constraint limiting the number of items that may
be selected. We characterize the performance of the optimal Lagrangian
index policy and bound and show that, under mild conditions, these policies
and bounds are both asymptotically optimal for problems with many items;
tiebreaking plays an essential role in the analysis of these index policies
and has a surprising impact on performance. We also develop an efficient
cutting-plane method for solving the Lagrangian dual problem and develop an
information relaxation bound that improves on the standard Lagrangian
bound. We demonstrate these policies and bounds in two large-scale
examples: a dynamic assortment problem with demand learning and an
applicant screening problem. [Joint work with Jim Smith (Duke).]
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