Informs Annual Meeting 2017
INFORMS Houston – 2017
2 - Restoration of Services in Disrupted Infrastructure Systems: A Network Science Approach Aybike Ulusan, Northeastern University, 409 Huntington Avenue, Apt #17, Boston, MA, 02115, United States, firstname.lastname@example.org, Ozlem Ergun The goal in this study is to establish the functionality of the disrupted service networks operating on infrastructure systems in a timely manner by prioritizing the restoration operations. As an illustrative case study, we consider a road network blocked by debris in the aftermath of a natural disaster, where the services correspond to the emergency relief efforts flowing from relief providers to disaster sites. We propose a heuristic that prioritizes the restoration activities based on a novel centrality measure that captures the service related operational characteristics of the infrastructures. 3 - Integrating Location and Network Restoration Decisions in Relief Networks under Repair Time Uncertainty Ece Sanci, University of Michigan, 3552 Greenbrier Blvd, Apt 463C, Ann Arbor, MI, 48105, United States, email@example.com, Mark Stephen Daskin We propose a two-stage stochastic programming model to locate emergency response facilities prior to a disaster. These facilities are used to distribute emergency relief items to disaster victims using the road network after the disaster. We integrate location and network restoration decisions so that the unmet demand at inaccessible demand points can be met as paths between these demand points and facilities become available. We capture uncertainty in the network availability by incorporating the repair times required to restore the damaged roads. Our computational results show significant improvement in unmet demand and cost measures compared to the model omitting repair decisions. 342E Innovations in Revenue Management Sponsored: Revenue Management & Pricing Sponsored Session Chair: Goker Aydin, Johns Hopkins University, Baltimore, MD, 21202, United States, firstname.lastname@example.org Co-Chair: Shengqi Ye, Shengqi.Ye@utdallas.edu 1 - Real-time Spatial Dynamic Pricing for Balancing Supply and Demand in a Network George Chen, University of Texas at Dallas, Richardson, TX, United States, email@example.com, Yanzhe Lei, Stefanus Jasin Motivated by recent expansion of mobile ride-hailing apps in the taxi industry in big cities, we study a real-time spatial dynamic pricing problem where a firm who uses many units of reusable resources (e.g., taxis) in a network to serve price- sensitive customers who arrive over a finite selling season (e.g., one day) in a stochastic and nonstationary fashion. For any origin-destination pair, the quoted price equals a nominal price times an origin-specific price multiplier. The firm can dynamically change quoted prices by adaptively adjusting the price multipliers over time. We develop a Network Balancing Control that has asymptotically optimal performance and discuss some extensions. 2 - Price, Wage and Fixed Commission in On-Demand Matching Yun Zhou, Rotman School of Management, Toronto, ON, Canada, Yun.Zhou13@Rotman.Utoronto.Ca, Ming Hu Motivated by the emerging sharing economy, we study an on-demand matching platform which crowdsources a service from independent suppliers and sells it to customers. The platform offers a wage to the supply side and charges a price to the demand side. Under market uncertainty, we study the performance of the widely practiced, flat, across-the-board commission contracts, under which the platform takes a fixed cut and the wage is equal to a preset fraction of the price, regardless of what price is charged. 3 - Optimal Spending for a Search Funnel Shengqi Ye, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080, United States, firstname.lastname@example.org, Goker Aydin, Shanshan Hu Sponsored search marketing has been a major advertising channel for online retailers. Recent observation indicates that not all customers finalize their purchase decision after their first search query. Instead, customers might take a path of keywords and clicks - a search funnel - to complete a conversion. Noting this behavior, we investigate a retailer’s optimal advertising budget allocation across keywords in the search funnel. TC23
342F Topics in Revenue Management and Pricing I Sponsored: Revenue Management & Pricing Sponsored Session Chair: Stefanus Jasin, University of Michigan, Ann Arbor, MI, 48105, United States, email@example.com 1 - Dynamic Inventory Control with Stockout Substitution and Demand Learning Boxiao (Beryl) Chen, University of Illinois at Chicago, Chicago, IL, United States, firstname.lastname@example.org, Xiuli Chao Stock-out substitution is the phenomenon that if the first-choice product of a customer is out of stock, besides leaving the market immediately, the customer may also substitute for other products. In this paper, we study a data-driven inventory management problem and infer the customer substitution behavior from historical sales data. 2 - Assortment Optimization with Small Consideration Sets Jacob Feldman, email@example.com, Alice J. Paul, Huseyin Topaloglu We study a customer choice model that captures purchasing behavior when customers substitute a limited number of times. Under this model, we assume each customer is characterized by a ranked preference list of products and that upon arrival, they will purchase the highest ranking product in her list that is offered. We assume that these rankings contain at most k products. This paper focuses on the assortment optimization problem under this choice model. First, we show that this problem is NP-hard even for k=2. Motivated by this result, we then develop a novel linear programming based rounding algorithm for the assortment optimization problem for general k. 3 - Design of Futures Contracts for Risk Averse Internet Advertisers Antoine Desir, Columbia IEOR.Department, 601 W. 113th Street, Apt 3J, New York, NY, 10025, United States, firstname.lastname@example.org, Maxime Cohen, Nitish Korula, Balasubramanian Sivan We consider the problem of selling ad impressions to risk-averse advertisers via Internet display advertising platforms. Advertisers’ buying choices typically include two options: Either they commit to a reservation contract in advance, or they buy programatically in real time via an exchange. In this paper, we formally study how risk aversion affects the desire for guarantees, and their pricing. We propose an additional programmatic purchase option (called Market Maker) which acts like an automated reservation. We show that adding the Market- Maker contract results in a Pareto improvement in the seller revenue and in the sum of advertiser utilities. 4 - Joint Dynamic Pricing and Order Fulfillment for E-commerce Retailers Yanzhe (Murray) Lei, University of Michigan, Ann Arbor, MI, United States, email@example.com, Stefanus Jasin, Amitabh Sinha We consider a joint dynamic pricing and order fulfillment optimization problem in the context of e-commerce retail, where a retailer sells multiple products to customers from different locations and fulfills orders through multiple fulfillment centers. The objective is to maximize the total expected profits, defined as the revenue minus the shipping cost. We propose two heuristics that are easily implementable and show both theoretically and numerically their good performances compared to reasonable benchmarks.
350A Strong and Extended Formulations for Unit Commitment Problems Invited: Energy Systems Management Invited Session
Chair: Yongpei Guan, University of Florida, 303 Weil Hall, P.O. Box 116595, Gainesville, FL, 32611, United States, firstname.lastname@example.org 1 - Polynomial Time Algorithms and Extended Formulations for Unit Commitment Problems Kezhuo Zhou, University of Florida, 411 Weil Hall, Gainesville, FL, 32611, United States, email@example.com, Kai Pan, Yongpei Guan In this talk, we develop polynomial time dynamic programming algorithms for unit commitment(UC) problems with general convex or piecewise linear cost functions. The algorithms are also extended to stochastic UC with piecewise linear cost. Extended formulations and physical insights are explored based on these dynamic programming algorithms.
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