Informs Annual Meeting 2017

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INFORMS Houston – 2017

6 - Operational Transparency and Slow Fashion Maneesh Reddy Ajjuguttu, Clemson University, 515 Calhoun Dr, Sirrine Hall, Room # 400, Clemson, SC, 29634, United States, majjugu@g.clemson.edu, Aleda Roth This study addresses the effects of operational transparency on the fashion sector. In particular, we examine the consumer buying behaviors on the emerging movement called “slow fashion”. Slow fashion is a strategy that aims to reduce the toxic and non-toxic wastes that accrue through the supply chains, which are causing havoc to the environment. 342D Data-Driven Approaches in Pricing and Revenue Management Sponsored: Revenue Management & Pricing Sponsored Session Chair: Mikhail Nediak, Queen’s University, Kingston, ON, K7L 3N6, Canada, mnediak@business.queensu.ca Co-Chair: Yuri Levin, Queen’s University, Kingston, ON, K7L 3N6, Canada, ylevin@business.queensu.ca 1 - Data-driven Approaches to Live Market Test Design Gabrielle Shklovsky, Senior Operations Research Consultant, Revenue Analytics, 300 Galleria Parkway Suite 1900, Atlanta, GA, 30339, United States, GShklovsky@revenueanalytics.com A live market test of a new pricing strategy or revenue management system is often used to measure the impact of the strategy or system before a full roll out. The success or failure of a market test is highly influenced by its design - what time period is used, what is assigned to test vs control, what metrics are used for evaluation - and these design decisions can be tricky, especially when business or operational constraints prevent full randomization. This talk will demonstrate how statistical simulations can be used to design market tests that increase the likelihood of success by maximizing signal and minimizing noise. 2 - Network Revenue Management in Contractual Setting Akram Khaleghei, Scotiabank, 40 King’s West, Toronto, ON, Canada, Akram.Khaleghei@scotiabank.ca, Mikhail Nediak, Ivan Sergienko We consider an allocation problem for a collection of limited divisible resources. The decision maker observes booking requests from finite set of clients for different type of products and has to commit resources to generate revenue. Once resources are committed, they have to remain committed until a specified maturity date. Requests arrive randomly over time with an intensity that depends on acceptance/rejection of customer previous requests. We are interested in finding the acceptance policy that maximizes the total expected revenue over a finite time horizon. 3 - Integrated Risk Management in the Airline Industry David Pyke, University of San Diego School of Business, San Diego, CA, 92110, United States, davidpyke@sandiego.edu, Ruixia Shi, Soheil Sibdari, Wenli Xiao We study integrated decision making in the airline industry. Most airlines make decisions about hedging fuel cost, capacity, and airfare pricing in a decentralized way. We model the benefits that accrue to a firm that maximizes its profit by integrating these decisions. We study the impact of economic conditions, stochastic market demand, consumer price sensitivity, and stochastic fuel cost on the three decisions. 342E Topics in Revenue Management and Pricing 2 Sponsored: Revenue Management & Pricing Sponsored Session Chair: Stefanus Jasin, University of Michigan, Ann Arbor, MI, 48105, United States, sjasin@umich.edu 1 - Dynamic Pricing for Hotel Rooms when Customers Request Multiple-day Stays Yun Fong Lim, Singapore Management University, Lee Kong Chian School of Business, 50 Stamford Road, 04-001, Singapore, 178899, Singapore, yflim@smu.edu.sg, Selvaprabu Nadarajah, Qing Ding We study the dynamic pricing problem faced by a hotel that maximizes expected revenue from a single type of rooms. Demand for the rooms is stochastic and non-stationary. Our Markov decision process formulation of this problem determines the optimal booking price of rooms (resources) for each individual WC22 WC23

day, while considering the availability of room capacity throughout the multiple- day stays (products) requested by customers. We develop an approximate linear programming based pricing policy, which outperforms both a fixed-price heuristic and a single-day decomposition approach. Specifically, it generates up to 7% more revenue than the single-day decomposition approach. 2 - Strategic Surge Pricing and Communication on On-demand Service Platforms Harish Guda, University of Texas-Dallas, 2700 Waterview Parkway, On-demand platforms often rely on the gig-economy, a pool of independent workers who work at their convenience, to serve consumers at short notice. To ensure that workers are at the right locations at the right times, on-demand platforms typically adopt two strategies: sharing market forecasts with workers and surge pricing. We jointly examine both strategies, explicitly accounting for workers’ incentives to move between market locations, and the platform’s incentive to share market information truthfully. Contrary to conventional wisdom, we show that surge pricing can be profitable for the platform even at a location where supply exceeds demand. 3 - Spatial Pricing and Inventory Allocation Long He, National University of Singapore, Mochtar Riady Building, BIZ1 8-73, 15 Kent Ridge Drive, Singapore, 119245, Singapore, longhe@nus.edu.sg, T. Tony Ke We study how an online retailer of multiple products should allocate its product inventories to facilitate spatial price discrimination. We consider two common spatial pricing policies: free on board (FOB) pricing that charges each consumer the exact amount of shipping cost, and uniform delivered (UD) pricing that provides free shipping. From the analysis of a stylized model and numerical experiments of optimization models, we find that compared with UD, FOB pricing yields higher profit—-the average relative profit difference between FOB and UD pricing is about 13%, when the inventory allocation is jointly optimized. 4 - Real-time Dynamic Pricing for Revenue Management with Reusable Resources and Deterministic Service Time Requirements Yanzhe (Murray) Lei, University of Michigan, Ann Arbor, MI, United States, leiyz@umich.edu, Stefanus Jasin We study a dynamic pricing problem where a firm faces price-sensitive customers arriving stochastically over time. Each customer consumes one unit of resource for a deterministic amount of time, after which the resource can be immediately used to serve new customers. We develop two heuristic controls and show that both are asymptotically optimal in the regime with large demand and supply. We further generalize both of the heuristic controls to the settings with multiple service types requiring different service times and with advance reservation. 342F Joint Session Auctions/RMP: Mechanism Design, Networks, and New Markets I Sponsored: Revenue Management & Pricing Sponsored Session Chair: Santiago Balseiro, srb43@duke.edu Co-Chair: Ozan Candogan, University of Chicago, Chicago, IL, 27708, United States, ozan.candogan@chicagobooth.edu 1 - Pricing Network Effects under Noisy Network Information Ankur Mani, University of Minnesota, 808 Berry Street, Apt 410, Saint Paul, MN, 55114, United States, amani@umn.edu, Jiali Huang We study the problem of a firm that uses social network information to optimally provide personalized pricing customers based upon their position in the network. We show that in large random networks such discriminative pricing may not have much advantage as compared to uniform pricing and may even underperform when the network is noisy. We propose simple implementable pricing policies with guarantees on expected profit. 2 - A Case for Independent Campaign Management in Online Display Advertising Amine Allouah, Columbia University, 520 W 122nd Street, Apt24, New York, NY, 10027, United States, mallouah19@gsb.columbia.edu, Omar Besbes In many auctions, buyers can be represented by an intermediary that manages their bidding process, along with the bidding process of other buyers, leading to some form of collusion. We propose a framework to analyze the implications of such an active/centralized role by intermediaries in a general market. #4424, Richardson, TX, 75080, United States, hxg131530@utdallas.edu, Upender Subramanian WC24

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