Informs Annual Meeting 2017

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INFORMS Houston – 2017

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360D Revenue Management, Pricing Contributed Session Chair: Alejandro Lamas, NEOMA Business School, Rouen, France, alejandro.lamas@neoma-bs.fr 1 - Spatial Distribution of Surge Prices under Incentive Compatible Driver Assignment Shuangyu Wang, Columbia University, Rm 323 SW. Mudd Building, 500W. 120th Street, New York, NY, 10027, United States, sw2756@columbia.edu, Garud N.Iyengar, Vineet Goyal In our work, we propose a fluid model for computing the optimal spatial distribution of surge prices on ride-sharing platforms in the presence of temporal demand shocks, to manage the mismatch in supply and demand of rides. The goal of surge is to maximize total revenue or throughput over the traffic network and the platform can assign drivers to riders but is constrained to respect driver incentives with respect to assignment deviations. Our main contribution is to show the structural properties of optimal surge prices with an efficient computation of surge prices. 2 - Dynamic Pricing when Customers Learn the Quality of the Product Ruizhi Shi, University of Minnesota, 2600 University Avenue SE, Apt 413, Minneapolis, MN, 55414, United States, shixx588@umn.edu In this talk, we consider a revenue management problem when customers give review ratings of the products and take that into account when making purchase decisions. In particular, we consider a seller selling a single product to a sequence of customers. Customers’ utilities are based on both the price and other customers’ rating. Each time, one customer comes and gives a rating which is drawn from a certain distribution, and the rating of the product will be updated after each customer’s purchase. We derive optimal policy on how the seller should change its prices during the time horizon to maximize his revenue. We also derive lower bounds on the best performance of any policy. 3 - Competitive Revenue Management with Sequential Bargaining Yuanchen Li, Purdue University, 2192 Tortuga Lane, West Lafayette, IN, 47906, United States, li1820@purdue.edu, Qi Feng, J. George Shanthikumar We study the role of bargaining in competitive revenue management. The seller firms each have some fixed initial stocks and compete to sell to a random arrive stream of potential buyers who are heterogeneous in product valuation. We fully characterize the equilibrium outcome, which depends on the length of the selling season and the initial stock levels. When the selling season is up to three periods, the seller with a lower stock level is weakly preferred by an incoming buyer. When the selling season is longer than three periods, the seller with a higher stock level may win the competition. 4 - Dynamic Pricing for Multi-period Home Delivery Alejandro Lamas, Assistant Professor, NEOMA Business School, Rouen, 76130, France, alejandro.lamas@neoma-bs.fr We consider a firm that delivers goods (or provides service) to a set customers. When a customer requests a delivery, the firm charges a price based on the geographical location, the available capacity and the lead-time of the delivery. Since customers and firms agree on the lead-time of the delivery, the problem for the firm consists of setting prices for deliveries in a rolling-horizon. The problem suffers from the “curse of dimensionality”, thus we propose heuristic approaches for obtaining efficient solutions. 5 - Optimal Pricing for Selling to a Static Multi-period Newsvendor Xiao Alison Chen, University of Minnesota, 111 Church Street SE, This paper considers a multi-period supply chain model in which a supplier sells to a multi-period newsvendor. Such a problem is relevant in industries with long production lead times. We study the optimal pricing problem for the supplier. We derive procedures for solving the optimal prices and show that the optimal pricing sequence is decreasing in time. We also show that the optimal prices are increasing in the backorder cost when the cumulative demand functions have increasing generalized failure rates. 6 - Airline Switching Revenue with Price-guarantees Fouad H. Mirzaei, Santa Clara University, Unit 3, 559 Alviso St, Santa Clara, CA, 95050, United States, fhmirzaei@scu.edu Many airlines permit passengers to change the time of their flight by paying a switching fee. In this study, we model a firm which delivers two sequential homogeneous services and generates ancillary revenue from customers switching between two services. Without imposing any distributional assumptions, we analyze the firm’s revenue function and derive the optimal switching fee. We also investigate the impact of the uncertainty in switching behavior of customers on the ideal switching policy. ME 1101, Minneapolis, MN, 55455, United States, chen2847@umn.edu, Zizhuo Wang, Hongsong Yuan

360E Scheduling in Project Environment Invited: Project Management and Scheduling Invited Session 1 - Resource-Constrained Dynamic Programming with”Hot-Starting” for the Elementary Shortest Path Problem Luis Novoa de Oro, The George Washington University, Washington, DC, United States, ljnovoa@gwu.edu, Ahmad I. Jarrah, Jonathan F. Bard, Daniel Chudnov New hot-starting procedures to significantly improve the efficiency of a dynamic program (DP) for solving the Resource Constrained Elementary Shortest Path Problem are implemented and tested. The DP is integrated with bidirectional extensions, decremental state-space relaxation, 2-cycle elimination and a We show that the schedule for contracting public procurements can impact the number of proposals and thereby the cost of procured services or products. We formalize this previously unexplored problem of scheduling procurement contracting and develop an optimization framework to solve it. Furthermore, we develop heuristics that do not require solving optimization problems, such as (i) equal number with mixed cost scheduling heuristic and (ii) equal cost scheduling heuristic. We apply our optimization framework to the Florida Department of Transportation’s procurement contracts scheduling problems and show that it yield significant cost savings over the actual schedules employed in practice. 3 - Establishing Actionable Visibility in Project Scheduling under Optimism, Heuristics, and Algorithmic Biases Byung-Cheol Kim, Penn State, Erie, PA, United States, buk70@psu.edu Establishing an actionable visibility into the future in terms of effective scheduling is a crucial challenge for successful management of a project. This session presents analytical approaches to establish the actionable visibility required for the project manager to make informed decisions under optimism, heuristics, and algorithmic biases. The presentation starts with illustrative examples of the situations where the project visibility is less actionable with incomplete and often biased information. Then a set of analysis tools is formulated based on best practices and proven techniques in project scheduling, risk analysis, and systematic error controls. sharpest-to-date restricted set of unreachable nodes. 2 - Scheduling Public Requests for Proposals Janne Kettunen, The George Washington University, Department of Decision Sciences, 2201 G.Street NW, Washington, DC, 20052, United States, jkettune@gwu.edu, Young Hoon Kwak 360F Economics of Online Platforms Sponsored: EBusiness Sponsored Session Chair: Hossein Ghasemkhani, Purdue University, 425 W State Street, West Lafayette, IN, 47907, United States, hossein@purdue.edu 1 - The Advertising Big Picture: Analyzing the Cross Platform Synergies Between TV and Online Advertising Mohammed S. Alyakoob, Purdue University, 2165 Cushing Drive, West Lafayette, IN, 47906, United States, malyakoo@purdue.edu The advent of the internet naturally gave rise to digital advertising, which provides a unique and insightful avenue for researchers and practitioners to monitor customers’ responses to a multitude of different types of advertisements. This research seeks to utilize this potential in combination with a detailed television advertising data set to gain insights regarding the synergies that exist between television and online advertising platforms and the way these interactions affect customer behavior. We study the relationships between the online and television advertising platforms and the impact these synergies have on a customer’s propensity to purchase (convert) in an online setting. TB47

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