2016 INFORMS Annual Meeting Program

TB47

INFORMS Nashville – 2016

TB47 209C-MCC Pricing, Promotions and Bundling for Revenue Management Sponsored: Revenue Management & Pricing Sponsored Session

3 - Multi-objective Optimization Via Simulation On Integer-ordered Spaces

Kalyani S Nagaraj, Purdue University, West Lafayette, IN, United States, kalyanin@purdue.edu, Kyle Cooper, Susan R Hunter Consider the context of multi-objective optimization via simulation when the search space is integer-ordered. We propose a framework to efficiently identify the Pareto set that solves a sequence of stochastically constrained problems (via the epsilon-constraint method) and is designed for deployment on a parallel computing platform. We discuss the design principles that make our framework efficient. 4 - Parallel Empirical Stochastic Branch And Bound Jie Xu, George Mason University, 4400 University Dr., MS4A6, Fairfax, VA, 22030, United States, jxu13@gmu.edu, Scott L. Rosen, Peter Salemi, Sajjad Taghiyeh In this talk, we show how the Empirical Stochastic Branch and Bound (ESSB) algorithm, which is an effective globally convergent random search algorithm for discrete optimization via simulation, can be adapted to a high-performance computing environment to effectively utilize the power of massive parallelism. We propose a master-worker structure driven by MITRE’s Goal-Directed Grid- Enabled Simulation Experimentation Environment. We present numerical experiments with a benchmark test function and a real-life simulator to test the scalability of parallel empirical stochastic branch and bound. TB46 209B-MCC Choice Modeling and Assortment Optimization Sponsored: Revenue Management & Pricing Sponsored Session Chair: Huseyin Topaloglu, Cornell Tech, 111 8th Avenue, Suite 302, Ithaca, New York, NY, 10011, United States, ht88@cornell.edu We consider the static assortment optimization problem where the objective is to determine the profit/revenue maximizing subset of products from a large universe of products. The product prices are exogenously fixed and the demand follows a general choice model. The problem in general is NP-hard and the greedy algorithm has been found to have good practical performance. We study the performance of a local search heuristic and show that it reaches the optimal solution for the MNL model and derive approximation guarantees for the random parameters logit (RPL) and the nested logit (NL) models. Numerically, we show that the algorithm outperforms existing heuristics in a wide-range of settings. 2 - Robust Assortment Optimization Under The Markov Chain Model Antoine Desir, Columbia University, ad2918@columbia.edu, Vineet Goyal, Bo Jiang, Huseyin Topaloglu, Tian Xie, Jiawei Zhang In this paper, we consider a robust assortment optimization problem under the Markov Chain model. In that setting, the true parameters of the model are unknown and belong to some uncertainty set. The goal is to select an assortment that maximizes the worst-case expected revenue over all parameter values. We present an efficient algorithm to compute the optimal robust assortment when the uncertainty set is row-wise. That is naturally the case in many settings. Our algorithm provides interesting operational insights regarding addressing uncertainty in the Markov chain model. 3 - On The Structure Of Cardinality-constrained Assortment Optimization Problems Louis L Chen, Massachusetts Institute of Technology-ORC, llchen@mit.edu, David Simchi-Levi Cardinality-Constrained assortment optimization, the problem of offering an assortment of items of constrained size that will maximize expected revenue, is generally regarded as a challenging problem. We provide a new perspective to the structural analysis, one that illuminates the optimality of “greedy solutions.” The approach reinterprets some known results for standard choice models but also provides some new ones as well. 4 - Competitive Pricing Under The Markov Chain Choice Model Huseyin Topaloglu, Cornell Tech, New York, NY, 10011, United States, ht88@cornell.edu, James Dong We consider competitive pricing problems under the Markov chain choice model. In this choice model, the customer transitions between the products according to a transition probability matrix. Based on the price of the product she visits, she decides to purchase the product or not. In our competitive setting, the prices of the different products visited by the customers are controlled by different firms. Each firm wants to maximize its own expected revenue. We show that a Nash equilibrium exists and the equilibrium prices are lower than those charged by a central planner. 1 - Assortment Optimization Under General Choice Srikanth Jagabathula, NYU Stern School of Business, sjagabat@stern.nyu.edu

Chair: Pelin Pekgun, University of South Carolina, 1014 Greene Street, Columbia, SC, 29208, United States, pelin.pekgun@moore.sc.edu 1 - A Pricing Model To Optimize The Promotions Period In Airlines Daniel Felipe Otero Leon, Lecturer, Universidad de los Andes, Bogota, 1111, Colombia, df.otero128@uniandes.edu.co, Cristina Lopez, Mariana Escallon, Raha Akhavan-Tabatabaei Promotions help increment the demand for a flight. Several decisions have to be made to offer a promotion such as its price and duration. We propose a method to estimate the behavior of customer inter-arrival time distribution, his buying probability distribution, and the dilution effect from data and develop a stochastic dynamic model to maximize the revenue, evaluating the decision of whether or not to offer the promotion. Finally we study the structural properties of the model and draw conclusions. 2 - Dynamic Pricing For Hotel Rooms When Customers Request Multiple-day Stays We study the dynamic pricing problem faced by a hotel that maximizes expected revenue from a single type of rooms. Demand for the rooms is stochastic and non-stationary. Our Markov decision process formulation of this problem determines the optimal booking price of rooms (resources) for each individual day, while considering the availability of room capacity throughout the multiple- day stays (products) requested by customers. To offer attractive average daily prices, the hotel should not only substantially raise the booking prices for some high-demand days, but also significantly lower the booking prices for the low- demand days that are immediately adjacent to these high-demand days. 3 - On The Benefit (or Cost) Of Large-scale Bundling Tarek Abdallah, New York University, tabdalla@stern.nyu.edu We study the effectiveness of a simple bundling mechanism in extracting the consumer surplus in the presence of non-negative marginal costs and correlated valuations. We develop simple robust analytics that identify the main drivers for the effectiveness of the pure bundling mechanism and allow the sellers to easily quantify the potential profits of a large-scale bundling mechanism relative to more complicated selling mechanisms. Our numerical simulations show that these analytics provide high predictive power for the true performance of the bundling mechanism and are robust to different parametric assumptions even for relatively small bundles. 4 - How Perceptions Of User Reviews Impact Price Competition Pelin Pekgun, University of South Carolina, Columbia, SC, 29208, United States, Pelin.Pekgun@moore.sc.edu, Michael Galbreth, Bikram Ghosh We analyze the interaction of user reviews and experience uncertainty, where negative and positive reviews may be weighted differently in a consumer’s assessment of the valence of the posted reviews. We find that the competitive impact of this unequal weighting may not be intuitive in terms of pricing and profits. In particular, if consumer awareness is higher for the lower quality product, it can charge higher prices and realize higher profits in equilibrium than its higher quality competitor when consumers are strongly influenced by negative reviews. Yun Fong Lim, Singapore Management University, yflim@smu.edu.sg, Selvaprabu Nadarajah, Qing Ding

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