Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

TC53

Control Unit -which controls the gateline) to occupy a more customers’ engaging role represent the most important outcome of this study. Using the Queueing Theory, the minimisation of the overall customers waiting times will be considered as the objective function of the optimisation model while some of the remaining key performance indicators will be placed as constraints. In order to reduce the mathematical complexity, the convexity of the mathematical model of the objective function is established. The objective function of the gateline con?guration being convex, the global optimum is found by computing the minima. 12 - Analysis of Economic Resiliency for Disaster Recovery Hulya Julie Yazici, Professor of Operations & Supply Chain Management, Florida Gulf Coast University, 10501 FGCU South Blvd, Fort Myers, FL, 33957, United States, Chris Westley, Anushka Chang This research is based on the last twenty- six years of hurricane activity at the state of Florida and the economic indicators affecting resiliency. The analysis is conducted at prior, during and post disaster years. This presentation describes the research variables, data collection and research hypotheses. At the time of the presentation, preliminary analysis results will be available and shared with the audience for their feedback. Analysis results are expected to improve disaster recovery and help businesses find sustainable solutions. n TC53 North Bldg 232A Joint Session AMD/RMP: Mechanism Design, Networks, and New Markets II Sponsored: Auction and Marketing Design Sponsored Session Chair: Ozan Candogan, University of Chicago, Chicago, IL, 27708, United States 1 - Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework Thodoris Lykouris, PhD Candidate, Cornell University, 107 Hoy Road, Gates 336, Ithaca, NY, 14853, United States, Siddhartha Banerjee, Daniel Freund Optimizing shared vehicle systems is more challenging compared to traditional resource allocation due to complex network externalities. In particular, pricing/rebalancing in any location affects future supply throughout the system within short timescales. Such externalities are captured by steady-state Markovian models. However, using such models to design control policies is computationally difficult since the resulting optimization problems are high- dimensional and non-convex. To this end, we develop a general approximation framework that provides the first efficient algorithms with rigorous approximation guarantees for a wide range of objective functions and controls. 2 - Revenue Management on an On-demand Service Platform Vijay Kamble, University of Illinois at Chicago, 405 N. Wabash Ave, Unit 3511, Chicago, IL, 60611, United States I consider the optimal pricing problem faced by a worker on an on-demand service platform. Service requests arriving while the worker is busy are lost. Thus, the optimal hourly prices need to capture the hourly opportunity costs incurred by accepting jobs. Due to potential asymmetries in these costs, price discrimination across jobs may be necessary for optimality, even if the customers’ preferences are identically distributed. I first establish that such price discrimination is not necessary if the customer arrival process is Poisson. I then consider the case of multiple customer classes. I present a simple procedure to compute the optimal prices in this case under standard regularity assumptions. 3 - Optimal Monitoring Schedule in Dynamic Contracts Mingliu Chen, Duke University, Durham, NC, United States, Peng Sun, Yongbo Xiao A principal induces effort from an agent to reduce the arrival rate of a Poisson process of adverse events. The effort is costly to the agent, and unobservable to the principal, unless the principal is monitoring the agent. Monitoring ensures effort but is costly to the principal. The optimal contract involves monetary payments and monitoring sessions that depend on past arrival times. The optimal schedules of payment and monitoring demonstrate different structures depending on model parameters, and may involve monitoring for a random period of time. Overall, the optimal dynamic contracts are simple to describe, easy to compute and implement, and intuitive to explain. 4 - Optimal Forecast Disclosure in Ride-sharing Platforms Peng Shi, University of Southern California, USC Marshall School of Business, BRI 303D, 3670 Trousdale Pkwy, Los Angeles, CA, 90089, United States, Hao Sun We study whether ride-sharing platforms such as Uber and Lyft should share their forecast of demand with drivers, if the goal is to maximize platform profit. In a stylized model, we show that the optimal forecast disclosure policy is tied to the accuracy of the forecast and the flexibility of the pricing system. When forecast is inaccurate and pricing is fixed, the platform has incentives to hide forecast from

drivers. However, as the forecast improves, the incentive to hide disappears. In the extreme case of perfect accuracy, it is always optimal to fully reveal the forecast. Full revelation is also optimal under imperfect forecasts if the platform can do forecast-contingent dynamic pricing.

n TC54 North Bldg 232B Behavioral Operations with Societal Impact Sponsored: Behavioral Operations Management Sponsored Session Chair: Leon Valdes, University of Pittsburgh, Pittsburgh, PA, 15260, United States 1 - Risk and Fairness Paola Mallucci, University of Wisconsin, Madison, WI, United States, Jordan D. Tong, Cuilty Emilio Vertical negotiations often fail because firms fail to agree on how to fairly compensate the party that faces the higher risk leading to large efficiency losses. While both preferences for fairness and for risk have been studied separately in vertical negotiation, little attention has been given to their combined effect. We address this gap by solving and testing a model where suppliers decide on the price of a good. We make predictions that account for both fairness and risk aversion and test them in a set of incentive compatible experiments. We find that subjects’ actions in the presence of risk cannot be explained by models in which preferences are consistent across roles and risk situations. 2 - Centralized or Decentralized Transfer Prices: A Behavioral Approach for Improving Supply Chain Coordination Sebastian Villa, University of Los Andes, Cra. Bogota, Colombia, Elena Katok When a retail channel includes multiple retailers, transshipments can be utilized to better match supply and demand. We study transshipment decisions in a channel with one supplier and two behavioral retailers under both centralized and decentralized transfer-price settings. We find that using the theoretical price does not help to increase neither profits nor coordination. However, by setting a behavioral transfer price, subjects place ordering decisions that lead to high supply chain profits. 3 - Patient Misinformation: The Costs and the Remedy Arshya Feizi, Boston University, 595 Commonwealth Avenue, Boston, MA, 02215, United States, Anita L. Tucker, Jillian Berry Jaeker We explore the increased healthcare costs due to patient misinformation with their caregiver and devise a screening policy to mitigate these costs. We focus on occasions when patients misinform their caregivers about their medication compliance and how this may increase their healthcare spending due to an increased risk of hospitalization. We investigate a screening mechanism that informs the caregiver about the true state of medication compliance. 4 - Trying and Failing: Donor Aversion to Rejection Kaitlin Daniels, Olin Business School, Washington University in St. Louis, Saint Louis, MO, United States, Leon Valdes The outpouring of concern that follows a disaster often generates excess donations. Nonprofits routinely accept these donations, fearing that turning away donors would discourage them from donating again in the future. We interrogate the extent to which this fear is warranted and study the impact of this behavior on nonprofits’ inventory management. We explore some of the behavioral motives for not donating again, as well as possible interventions to mitigate donor aversion to rejection. n TC55 North Bldg 232C Managing Uncertainties in New Product Development and Business Process Innovations Emerging Topic: New Product Development Emerging Topic Session Chair: Janne Kettunen, The George Washington University, Washington, DC, 20052, United States 1 - Impact of Queue Removing Technology on Competitive Retail Onesun Steve Yoo, University College London, School of Management, Gower Street, London, WC1E 6BT, United Kingdom, Adam Smith We analyze the impact of physical retailers removing the consumer’s need for queuing during checkout via new technologies such as Amazon Go or Mishipay. We examine value added for the retailers in a competitive retail environment and

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