Informs Annual Meeting 2017

TB25

INFORMS Houston – 2017

4 - Incentive Issues in the Implementation of Urban Consolidation Centers Xin Fang, Singapore Management University, 50 Stamford Road, #05-01, Singapore, 178899, Singapore, xfang@smu.edu.sg, Yun Fong Lim The growth of e-commerce worsens the traffic congestion in cities. An urban consolidation center (UCC) is a potential solution, but many UCC projects have failed due to the reluctance of carriers to cooperate. In this paper, we develop a game-theoretical model that captures carriers’ incentives. We find that the price that carriers are willing to pay for UCC’s service is too low for UCC to make a profit. We propose that UCC can work as information intermediary among carriers for capacity sharing. In this case, UCC can always make a profit from commissions, and carriers have stronger incentives to cooperate. 342E Revenue Management in Fashion Retailing Sponsored: Revenue Management & Pricing Sponsored Session Chair: Kris Johnson Ferreira, Harvard Business School, Boston, MA, 02163, United States, kferreira@hbs.edu 1 - Pass-through Constrained Vendor Funds for Promotion Planning Lennart Baardman, Massachusetts Institute of Technology, Operations Research Center, 77 Mass Ave, Bldg E40-130, Cambridge, MA, 02139-4307, United States, baardman@mit.edu, Georgia Perakis, Kiran Venkata Panchamgam Pass-through constrained vendor funds are trade deals in which suppliers offer discounts to retailers, encouraging them to promote their products, while demanding pass-through to the customers. First, we analyze the supplier’s strategic model about what vendor fund to offer. We show that pass-through constrained vendor funds eliminate forward-buying behavior and coordinate supply chains. Additionally, we model the retailer’s operational problem about which vendor funds to select. To solve this model we propose a fast Lagrangian relaxation approach with analytical performance guarantees. 2 - Using Clickstream Data to Improve Flash Sales Effectiveness Victor Martinez de Albeniz, IESE Business School, Av Pearson 21, Barcelona, 08034, Spain, valbeniz@iese.edu, Arnau Planas, Stefano Nasini Flash sales retailers organize online campaigns where products are sold for a short period of time at a deep discount. The demand in these events is very uncertain, but clickstream data provides very detailed information about the shopping process. We build a model for shoppers’ sequential decisions about visiting a campaign, obtaining product information and placing a purchase, which we validate using a large data set from a leading flash sales firm. Our forecasting model allows flash sales retailers to learn about the performance of new products in a few hours. We use this information to update prices so as to better match supply and demand forecast, and increase profits, on average by 20%. 3 - Assortment Rotation and the Value of Concealment Kris Johnson Ferreira, Harvard Business School, Morgan Hall 492, Boston, MA, 02163, United States, kferreira@hbs.edu We study one reason why frequent assortment rotations can be valuable. Namely, by distributing its seasonal catalog of products over many assortments, the retailer conceals a portion of its product catalog from consumers, injecting uncertainty into the consumer’s relative product valuations. Rationally acting consumers may respond to this uncertainty by purchasing more products, generating additional sales for the retailer. We refer to this phenomenon as the value of concealment. We develop a consumer choice model and stochastic dynamic program to study the value of concealment. 342F Topics in Revenue Management & Pricing Sponsored: Revenue Management & Pricing Sponsored Session Chair: N. Bora Keskin, Duke University, Durham, NC, 27708-0120, United States, bora.keskin@duke.edu 1 - Strategic Experimentation on Networks TB23 TB24

experimenting with new technology. At any time, an agent must decide whether to exert costly effort to “experiment” with the unknown project, or to wait in hope to see the outcome from other agents. We study this game in a networked setting, in which agents only have access to information from their neighbors. We find that agents with access to more information free-ride in optimistic scenaria, but work more in pessimistic scenaria. 2 - Learning Customer Preferences through Crowdvoting Yifan Feng, The University of Chicago, 5807 S.Woodlawn Avenue, Chicago, IL, 60637, United States, yfeng4@chicagobooth.edu, Rene A. Caldentey, Christopher T. Ryan A firm is launching a new product with multiple potential designs. To pick the design most preferred by customers, the firm invites potential customers to vote for their favorite designs. We study how to dynamically customize each voter’s choice set, in order to most efficiently learn overall customer preferences. 3 - Dynamic Pricing for Batch Jobs in the Cloud Hossein Jahandideh, 3777 Mentone Avenue, Apt 405, Los Angeles, CA, 90034-6473, United States, hs.jahan@gmail.com A growing model for selling cloud computing services is for the cloud provider to accept batch jobs such as machine learning, classification, large-scale optimization, etc. and commit to finishing the job by a certain deadline. The provider receives service requests and quotes a menu of prices for different completion times, based on the job type and resources required to fulfill the job. This amounts to a dynamic network revenue management problem, for which we propose a dynamic pricing mechanism and demonstrate its effectiveness. 4 - Discontinuous Demand Functions: Estimation and Pricing N. Bora Keskin, Duke University, Fuqua School of Business, 100 Fuqua Drive, Durham, NC, 27708-0120, United States, bora.keskin@duke.edu, Arnoud Victor den Boer Motivated by online price-rankings that create discontinuities in demand functions, we consider a dynamic pricing-and-estimation problem with an unknown and discontinuous demand function. We show that ignoring such discontinuities results in substantial loss of revenues, and construct near-optimal policies that can handle demand discontinuities. 350A Recent Development in Electricity Markets Invited: Energy Systems Management Invited Session Chair: Feng Qiu, Argonne National Laboratory, Lemont, IL, 60439, United States, fqiu@anl.gov 1 - Development of MISO Market to Embrace Current and Future Challenges Yonghong Chen, Midwest ISO, 720 City Center Drive, Carmel, IN, 46032, United States, ychen@misoenergy.org This presentation will first give an overview of recent developments at MISO to improve market clearing and market pricing efficiency. It will then discuss new initiatives to embrace future challenges. 2 - Multi-market Settlements and Computational Efficiency in Power System Planning Studies Clayton Barrows, NREL, Denver, CO, United States, Clayton.Barrows@nrel.gov The pathway towards modernized grids will consider enhanced coordination between markets and interconnected systems. However, traditional models of power systems represent one of two extremes: centrally-planned and operated models of an entire interconnection, or mostly-isolated market footprint models with little trade between neighboring systems. Neither of these approaches accurately reflects present or future power system operations. This research develops techniques to simulate decentralized coordination of operations and trade between entities while enhancing the computational efficiency of power system planning studies. 3 - A Market for Transactive and Deliverable Flexibility Based on Uncertainty Marginal Price Hongxing Ye, Cleveland State University, Cleveland, OH, United States, hye9@hawk.iit.edu The demand of flexibility increases dramatically with rapid growth of variable energy resource. We propose a new concept of transactive flexibility that can be bought and sold at the uncertainty marginal price (UMP). The proposed model positions deliverable flexibility cost-effectively, and its cost is allocated to flexibility consumers respecting a cost-causation principle. With a proposed solution approach, it provides an option to address emerging questions: 1) how to determine the flexibility amount while keeping ISO independent; 2) how to allocate deliverable flexibility to demander cost-effectively; 3) how to handle the high flexibility demand when the flexible resource is scarce. TB25

Ramandeep Randhawa, University of Southern California, Bri 401p, 3670 Trousdale Pkwy, Los Angeles, CA, 90089, United States, ramandeep.randhawa@marshall.usc.edu, Kimon Drakopoulos

We study a dynamic game with multiple agents who are working to discover the quality (type) of an unknown project. An example is multiple R&D teams

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