2016 INFORMS Annual Meeting Program

TB42

INFORMS Nashville – 2016

TB42 207D-MCC Learning, Estimation, and Experimentation in RM and Pricing Sponsored: Revenue Management & Pricing Sponsored Session Chair: Stefanus Jasin, University of Michigan, Ann Arbor, MI, United States, sjasin@umich.edu 1 - Demand Forecasting In The Presence Of Unobserved Lost Sales Shivaram Subramanian, IBM, subshiva@us.ibm.com, Pavithra Harsha We present an effective mixed-integer programming (MIP) based optimization approach to calibrating attraction demand models for pricing and revenue management applications using censored historical sales data. This single-step approach helps overcome some of the limitations present in prior approaches (e.g., the EM method). We discuss its practical viability by reviewing a recent commercial implementation for a large retail chain. We also comment on interesting extensions. 2 - Learning Valuation Distributions From Bundle Sales Will Ma, ORC, MIT, willma353@gmail.com, David Simchi-Levi Bundling has been widely studied in the literature as a form of price discrimination. We show that it can also be used as a form of price experimentation - a mixed bundling scheme allows the firm to quickly learn the customer valuation distributions without having to change any prices. We present an iterative algorithm to reverse-engineer the valuations based on bundle sales, with theoretical convergence guarantees for Uniform distributions. For other two- parameter families of distributions, our extensive numerical experiments demonstrate that optimizing over the learned parameters still extracts close to 100% of the optimal profit obtainable had we known the exact parameters. 3 - Less Can Be More In Price Experimentation Georgia Perakis, MIT, georgiap@mit.edu, Divya Singhvi We consider a dynamic pricing problem where a monopolist is selling a single product but has no knowledge of the demand curve. Further, there is a cost on price experimentation, as for every price the monopolist incurs a fixed operational cost. The monopolist seeks to efficiently learn the demand curve and keep the cost of price experimentation low. We propose an approach for price experimenting and learning the demand for the problem and provide bounds on the number of price experimentations needed to achieve a threshold revenue level for both parametric and non-parametric demand functions. We show that with few price experimentations (aka 4) we can be within 18% of the optimal. 4 - A Nonparametric Self-adjusting Control For Multi-product Pricing With Limited Resources Qi (George) Chen, Ross School of Business, University of Michigan, Ann Arbor, MI, United States, georgeqc@umich.edu, Stefanus Jasin, Izak Duenyas We study a multi-period network revenue management problem where the underlying demand function is unknown (in the nonparametric sense) to the seller who uses dynamic pricing to minimize expected revenue loss. It is known that the asymptotic revenue loss of any feasible pricing policy is O(k^{1/2}) (k indicates the size of the problem), but there is a considerable gap between this theoretical lower bound and the performance bound of all existing heuristics. We propose a Nonparametric Self-adjusting Control and show that it guarantees a revenue loss of O(k^{1/2+epsilon} log k) for any arbitrarily small epsilon>0, provided that the underlying demand function is sufficiently smooth. TB43 208A-MCC Panel: New Frontiers in Decision Analysis Practice and Theory Sponsored: Decision Analysis Sponsored Session Moderator: Franklyn Koch, Koch Decision Consulting, Eugene, OR, United States, kochfg@gmail.com Moderator: Melissa A. Kenney, University of Maryland, College Park, MD, United States, kenney@umd.edu 1 - New Frontiers In Decision Analysis Practice And Theory Franklyn Koch, Koch Decision Consulting, kochfg@gmail.com This panel of Decision Analysis practitioners and academicians will discuss some of the problems in Decision Analysis that they are struggling to solve. These would include decisions where the existing techniques & tools fall short, areas where practitioners are looking for new approaches & insights, and innovative ideas and techniques that may provide new insights into difficult or complex decisions. Panelists include: Greg Hamm, Berkeley Research Group; Babak Jafarizadeh, Statoil; Bill Klimack, Chevron.

TB44 208B-MCC Graphical Methods Sponsored: Decision Analysis Sponsored Session

Chair: Jeffrey M Keisler, University of Massachusetts - Boston, 100 Morrissey Boulevard, Boston, MA, 02125, United States, jeff.keisler@umb.edu 1 - Decision Circuits For Decision Analysis Debarun Bhattacharjya, IBM T. J. Watson Research Center, Yorktown Heights, NY, United States, debarunb@us.ibm.com, Ross D Shachter A decision circuit is a graphical representation that is syntactic, i.e. depicts summation, multiplication and maximization operations required to solve a decision problem. Decision circuits can be viewed as a representation of decision analysis computations and therefore generalize decision trees as well as other well-known graphical forms. In this talk, I will present advances in our research on the formulation and analysis of decision analysis problems using decision circuits. 2 - On Computing Probabilities Of Dismissal Of 10b-5 Security Class-action Cases Sumanta Singha, PhD Student, University of Kansas, 1654 Naismith Dr, Lawrence, KS, 66045, United States, sumanta.singha@ku.edu, Steve Hillmer, Prakash P Shenoy The main goal of this paper is to propose a probability model for computing probabilities of dismissal of 10b-5 securities class-action cases filed in U.S. Federal district courts. By dismissal, we mean dismissal with prejudice in response to the motion to dismiss filed by the defendants, and not eventual dismissal after the discovery process. The proposed probability model is a hybrid of two widely-used methods: logistic regression (LR), and naïve Bayes (NB). Using a dataset of 925 10b-5 securities class-action cases, we show that the proposed hybrid model has the potential of computing better probabilities than either LR or NB models. By In observation networks, agents make observations, make new inferences, and report to neighbors to ultimately identify correct alternatives. Report plans ensure that this happens reliably. Junction tree algorithms applied to Bayes networks constitute report plans. General conditions for existence of report plans suggest other modeling possibilities. TB45 209A-MCC Parallel Simulation Optimization Sponsored: Simulation Sponsored Session Chair: Jie Xu, George Mason University, 4400 University Dr., MSN 4A6, Fairfax, VA, 22030, United States, jxu13@gmu.edu 1 - Implementing A Ranking And Selection Procedure In The Cloud Sijia Ma, Cornell University, Ithaca, NY, United States, sm2462@cornell.edu, Shane Henderson The goal of ranking and selection (R&S) procedures is to identify the stochastic system with largest mean from among a finite set of competing alternatives. We are implementing a R&S algorithm within a commercial simulation software product that runs in the cloud. A cloud-computing implementation requires estimating the wall-clock running time as a function of the number of cores used. To estimate the running time we develop a sampling and estimation method to learn about the ordered means. This methodology allows us to predict the residual running time more and more accurately as the R&S algorithm proceeds, and may prove useful when estimating the running times of other R&S procedures. 2 - Speeding Up Sequential Selection-of-the-best Procedures For Large-scale Problems Jeff Hong, City University of Hong Kong, jeffhong@cityu.edu.hk, Jun Luo, Ying Zhong Classical sequential ranking-and-selection (R&S) procedures require all pairwise comparisons after collecting one additional observation from each surviving system, which is typically an O(k^2) operation where k is the number of systems. When k is large (e.g., millions), these comparisons can be very costly and may significantly slow down the R&S procedures. In this paper we revise KN procedure slightly and show that one may reduce the computational complexity of all pairwise comparisons to an O(k) operation, thus significantly reducing the computational burden. Numerical experiments show that the computational time reduces by orders of magnitude. better, we mean lower RMSE of probabilities of dismissal. 3 - Observing Reporting And Deciding In Networks Jeffrey Keisler, University of Massachusetts Boston, jeff.keisler@umb.edu, H Jerome Keisler

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