Informs Annual Meeting 2017

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INFORMS Houston – 2017

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drivers, the platform may need to set prices higher than those that would balance supply and demand. Our results show how flexible supply impacts the optimal pricing policy and how supply incentive constraints shape it. 2 - On-demand Service Platforms Terry Taylor, University of California Berkeley, 545 Student Services #1900, 2220 Piedmont Avenue, Berkley, CA, 94720, United States, taylor@haas.berkeley.edu An on-demand service platform (e.g., Uber, Instacart) connects waiting-time sensitive customers with independent service providers. This paper characterizes how two defining features of an on-demand service platform—congestion-driven delay and service provider independence—impact the platform’s optimal per- service price and wage. 3 - Pricing and Optimization in Shared Vehicle Systems Siddhartha Banerjee, Cornell University, 229 Rhodes Hall, Ithaca, NY, 14853, United States, sbanerjee@cornell.edu, Daniel Freund, Thodoris Lykouris We develop a framework for optimizing shared-vehicle systems, modeled using closed queueing networks. Our approach gives the first efficient algorithms with rigorous guarantees for several common demand-supply balancing controls, including pricing, rebalancing and matching, in the process simplifying and extending several recent results. Moreover, our framework, which is based on a novel convex relaxation coupled with a new infinite-projection and pullback technique, may prove useful for proving approximation bounds in other settings. 4 - Spatial Pricing of Taxi Rides Nasser Barjesteh, University of Chicago, 5242 S Kenwood We study the pricing of taxi rides based on their pickup location. We introduce a structural model that captures the Spatial and inter-temporal variations in demand and the distribution of the destinations of the customers, the distances between locations, and the strategic relocations of the taxis. The goal is to use the model with the estimated parameters to find the spatial pricing scheme that maximizes the consumer surplus. Avenue, Apt 2, Chicago, IL, 60615, United States, barjesteh@chicagobooth.edu, Baris Ata, Sunil Kumar 350A Innovation, Product, and Project Management Invited: New Product Development Invited Session Chair: Morvarid Rahmani, Georgia Institute of Technology, Atlanta, GA, 30308-1149, United States, morvarid.rahmani@scheller.gatech.edu 1 - Procurement Contracting under Product Recall Risk Yue Zhang, Duke University, 5507 Butterfly Lane, Apt 207, Durham, NC, 27707, United States, yueyue.zhang@duke.edu, Lauren Xiaoyuan Lu, Jayashankar M.Swaminathan, Gang Wang Managing product quality and mitigating the financial impact of product recalls pose great challenges to manufacturers due to demand uncertainty and non- contractibility of suppliers’ quality effort. To understand the interdependence of supply chain quantity and quality decisions, we develop a procurement contractual framework under both demand and recall risks. We consider a model in which a manufacturer outsources to a supplier the production of a component, which is subject to potential quality failure leading to a product recall. We analyze two settings: a pull system in which the supplier makes the quantity decision and a push system in which the manufacturer makes the quantity decision. 2 - Optimal Contracts in Decentralized Projects Theodore D. Klastorin, University of Washington, We consider a serial stochastic decentralized project where a client subcontracts the work to an independent contractor. Using a Stackelberg game, we analyze several different types of contracts including incentive, fixed price, and cost plus contracts. We show that certain types of contracts maximize the expected client profit and derive conditions that define the optimal parameters for these contracts. ISOM.Department, Box 353226, Seattle, WA, 98195-3226, United States, tedk@u.washington.edu, Michael R.Wagner SA25

342E Using Field Experiments to Optimize Operations Sponsored: Revenue Management & Pricing Sponsored Session Chair: Spyros Zoumpoulis, Paris, 75005, France, spyros.zoumpoulis@insead.edu Co-Chair: Ruomeng Cui, Bloomington, IN, 47401-7739, United States, cuir@indiana.edu 1 - The Value of Fit Information in Online Retail: Evidence from a Randomized Field Experiment Santiago Gallino, Dartmouth College, 100 Tuck Hall, Hanover, NH, 03755, United States, santiago.gallino@tuck.dartmouth.edu, Antonio Moreno Garcia Virtual fitting-room technologies can provide information about how a product fits a particular customer in a novel way and mitigate the stress that the existing information gap generates in the retailers supply chain. By implementing a series of randomized field experiments, we study the value of virtual fit information in online retail. 2 - Mobile Messaging for Offline Group Formation in Prosocial Activities: A Large Field Experiment Tianshu Sun, University of Southern California, 3670 Trousdale Parkway, Bridge Hall, BR.I.310B, Los Angeles, CA, 90089, United States, tianshus@marshall.usc.edu, Guodong (Gordon) Gao, Ginger Zhe Jin We study whether and how charities can use mobile messaging to leverage individuals’ social ties and encourage offline prosocial activities in groups. In particular, we run a randomized field experiment with 80,000 blood donors and study how behavioral interventions and economic rewards motivate offline group formation. We find that commonly used interventions like reminder message and individual reward are ineffective in motivating group donation. In contrast, a newly proposed group reward is effective, and tends to attract different types of donors. Structural estimation further reveals mechanisms, and suggests group reward is 4 times more cost-effective than rewarding individual donors. 3 - Field Experiments with Restaurant Table-top Technology: How Do We Select What We Eat? Dmitry Sumkin, INSEAD, Rue de Montebelo 10, Fontainebleau, 77300, France, dmitry.sumkin@insead.edu, Serguei Netessine We conduct field experiments using tablet-based restaurant menus which allow us to randomize location of dishes and design of the menu. We identify menu designs that allow restaurants to increase sales by exploiting customer search patterns using table-top technology. 4 - Optimizing Promotion Targeting using Field Experiments Spyros Zoumpoulis, INSEAD, Boulevard de Constance, Fontainebleau, 77305, France, spyros.zoumpoulis@insead.edu, Theodoros Evgeniou, Duncan I.Simester, Artem Timoshenko We investigate how firms can use the results of field experiments to optimize marketing decisions, training and testing our policies on a series of two large scale field experiments. We discuss how to optimize the targeting of promotions, how to efficiently evaluate targeting policies, and how to optimally target non- responders (i.e., people who did not respond to past promotions) using the timing of the responses of responders. 342F Ride-sharing Platforms 2 Sponsored: Revenue Management & Pricing Sponsored Session Chair: Ozan Candogan, University of Chicago, Chicago, IL, 27708, United States, ozan.candogan@chicagobooth.edu Co-Chair: Daniela Saban, Stanford GSB, Palo Alto, CA, 94304, United States, dsaban@stanford.edu 1 - Surge Pricing and its Spatial Supply Response Francisco Castro, Columbia University, New York, NY, United States, fcastro19@gsb.columbia.edu, Omar Besbes, Ilan Lobel Our work seeks to understand the structure of ride-sharing platforms’ pricing strategies in a geographical network with self-interested supply agents. We anchor our analysis on a Stackelberg game. The platform sets prices at every node and drivers react by choosing whether to relocate to different nodes. We establish that an equilibrium for the drivers’ game always exists, and we give structural properties of this equilibrium. Surprisingly, to give the “right” incentives to SA24

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