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.