Grant CMMI-0540143 Manufacturing Enterprise Systems
Robustness and Performance in Data-Driven Revenue Management
PI: Aurélie Thiele - Lehigh University
Objectives:
To develop and analyze data-driven models of uncertainty in
revenue management, which will:
•Incorporate the decision-maker’s risk preferences,
•Dynamically integrate experimental measurements into the
computational approach.
Motivation:
•Inventory management traditionally assumes the precise
knowledge of the underlying demand distributions and a riskneutral manager.
•In practice:
1. Not enough information is available to compute probabilities.
2. Managers are generally risk-averse.
Dynamic and Adaptive Algorithms:
We incorporate information revealed over time without requiring the
decision-maker to estimate the underlying distribution.
1. Dynamic adjustment of sample size (amount of historical data
taken into account) for general non-stationary processes
2. Clustering mechanism for cyclical demand processes and
comparison with traditional Holt-Winters algorithm.
3. Hybrid algorithm combining data and range forecasts to protect
against adverse events that have yet to be observed.
Project Personnel:
•PhD student: Gokhan Metan (graduated in 2008, now at American
Airlines)
•PhD student: Michael Dziecichowicz
•MS student: Daniela Caro (graduated in 2009)
•Undergraduate student: Phoebe Lai (funded by a REU supplement)
•Other undergraduate students: Sara Ellis (2006), Ipek Ozkanoglu
(2006-07), Christopher Barrett (2007), Victoria Berenholz (2007)
Robust Resource Allocation:
•Issue: when to expand capacity under
demand uncertainty. Expansion can only
occur once.
•Demand model based on Bass demand
function (see examples to the right).
We built a computer tool in
Visual Basic that determines
the optimal strategy when
we maximize the worst-case
Net Present Value over
several possible demand
curves. (REU work)
Hybrid Robust-Stochastic Approach:
•We investigate the benefits of using several range forecasts
(scenarios) coupled with a worst-case approach for each range, to
incorporate demand information beyond the single confidence
interval traditionally used in robust optimization.
•We focus on optimal scenario definition with respect to unit cost
parameters and establish the existence of a critical value in
probability of baseline scenario.
In a cost-minimization
approach, it is most important
for the probability of the
baseline scenario to exceed
the threshold. The objective
then is quite insensitive to its
actual value.
Publications:
1. An adaptive algorithm for the optimal sample size in the nonstationary data-driven newsvendor problem, by G. Metan and A.
Thiele, in Extending the Horizons: Advances in Computing,
Optimization and Decision Technologies, pp.77-96, Springer, New York,
2007.
2. A dynamic and data-driven approach to the newsvendor
problem under seasonal demand, by G. Metan and A. Thiele, in
Logistics Challenges in the Enterprise, pp.427-441, Springer, New
York, 2009.
3. Integrated Forecasting and Inventory Control for Seasonal
Demand, by G. Metan and A. Thiele, to appear in the book Operations
Research and Cyber-Infrastructure, Springer, New York, 2009.
4. Protecting the data-driven newsvendor against rare events: A
correction-term approach, by G. Metan and A. Thiele, conditionally
accepted in Algorithmic Operations Research.
5. The value of information in inventory management, by G. Metan
and A. Thiele, submitted.
6. Robust timing decisions of markups and markdowns, by D.
Caro and A. Thiele, in preparation.
Broader Impacts:
•Computer tool to test impact of decision-maker’s assumptions.
•Blog at http://engineered.typepad.com
•Project webpage at http://www.lehigh.edu/~aut204/datadriven.html
To incorporate information
revealed over time, we built
a computer tool in Visual
Basic that rules out the
implausible demand curves
based on confidence
intervals around each curve.
(REU work)
Pricing-based Revenue Management:
•We have started working on price-based revenue management, with
a focus on the optimal decision time of markups and markdowns in a
selling season in a robust framework.
•We compare the optimal decision times with those obtained in the
nominal model.
•Future work include data-driven approach to price-response function
and customized pricing based on historical bids.
Presentations:
•ICS conference, January 2009
•INFORMS annual meeting, October 2008
•Seminar at U. Michigan (Ross School of Business), June 2008
•INFORMS annual meeting, November 2007
•Seminar at Penn State (Industrial Engineering), October 2007
•ICS conference, January 2007
•Seminar at Rutgers (Industrial Engineering), December 2006
•INFORMS annual meeting, November 2006
Industry Collaborations:
•Currently working with a local company to implement research
results in a real-life framework using real data.
•Implementing a web-based tool to help industry consortium apply
data-driven techniques.