2016 INFORMS Annual Meeting Program

MA26

INFORMS Nashville – 2016

4 - Research Opportunities In Project Scheduling Rainer Kolisch, Technical University of Munich, rainer.kolisch@tum.de

2 - Managing Multichannel Delivery Of Healthcare Services: Case Of Telemedicine In Rural India Kraig Delana, London Business School, PhD Program Office, London, NW1 4SA, United Kingdom, kdelana@london.edu, Kamalini Ramdas, Sarang Deo Telemedicine is a potent intervention to improve healthcare access for difficult-to- reach populations. We investigate the impact of the introduction of rural telemedicine facilities on access to eye care for patients in rural India using more than 4 million patient visit observations from the largest eye care system in the world. In particular, we exploit growth in the network of telemedicine centers over time and space to identify changes in where and how early patients seek care using a difference-in-differences methodology. Our results have implications for effective multichannel delivery of complex services such as healthcare. 3 - Forecasting Demand For New Products: Combining Subjective Rankings With Historical Data Marat Salikhov, INSEAD, Boulevard de Constance, Fontainebleau, 77305, France, marat.salikhov@insead.edu Nils Rudi We combine subjective ranking inputs with historical data for new product demand forecasting. The methods yields good fit with data, both for order statistics of proportions of total demand and for predicting the actual demand. MA28 201B-MCC Online Retailing Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: Dorothee Honhon, University of Texas at Dallas, Richardson, TX, United States, dorothee.honhon@utdallas.edu Co-Chair: Xiajun Amy Pan, University of Florida, Gainesville, FL, United States, amy.pan@warrington.ufl.edu 1 - Probabilistic Selling For Vertically Differentiated Products: The Role Of Salience Quan Ben Zheng, University of Florida, quan.zheng@warrington.ufl.edu, Xiajun Amy Pan, Janice E Carrillo This paper studies probabilistic selling for vertically differentiated products, whereby consumers do not know the exact identity of a product until after making the purchase. Our work discovers the crucial role of consumers’ salient thinking behavior: consumers focus on and overweight the salient attribute of a product in their perception. We show that probabilistic selling can improve the seller’s profit with salient thinkers even when this strategy does not emerge with rational consumers. Consumers’ salient thinking behavior enables the seller to utilize the probabilistic product to transform the consumers’ choice context and direct their attention to quality. 2 - Maximizing Profitability In Online Retail Through Free Shipping Threshold Jiaqi Xu, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15217, United States, jiaqixu@wharton.upenn.edu, Gerard P Cachon, Santiago Gallino We present a data-driven model to analyze the profit implication of an online retailer’s free shipping threshold decision. A key component of our model derives from the empirical observation that customers often increase their basket size at checkout to qualify for free shipping (order padding). We find that a free shipping threshold policy is effective when the extra sales from order padding do not substantially reduce the total amount of future purchases, the retailer charges only a small portion of the fulfillment cost for orders that do not qualify for free shipping, and product handling costs for returns are low. 3 - Learning From Clickstream Data In Online Retail Bharadwaj Kadiyala, PhD Candidate, The University of Texas at Dallas, Richardson, TX, United States, bharadwaj.kadiyala@utdallas.edu, Dorothee Honhon, Canan Ulu We study the problem of an e-tailer who learns about consumer preferences from observing sales or clickstream data on his website in a Bayesian fashion. We use a ranking-based model to represent consumer choice for two types of products: basic products for which consumers have well-defined preferences and fashion products for which consumers discover their preferences via browsing. We prove that, when the e-tailer learns from clickstream data, it may be optimal to show products on the search page, but display them as unavailable later on their product information page. We also numerically estimate the value of learning from clickstream data versus learning from sales data only.

Operations Research has been applied in project scheduling for more than half a century. This talk summarizes achievements and outlines research opportunities.

MA26 110B-MCC Dynamic Matching

Invited: Auctions Invited Session Chair: John Dickerson, Carnegie Mellon University, 9219 Gates- Hillman Center, Pittsburgh, PA, 15213, United States, dickerson@cs.cmu.edu 1 - Dynamics Matching With Departures Maximilien Burq, Massachusetts Institute of Technology, We study dynamic matching in an infinite-horizon market with stochastic arrivals and departures, in which some agents are a priori more difficult to match than others. We analyze the effect of batching for policies that match agents through cycles of length 2 or 3. We show that if only cycles of length 2 are allowed, the benefit of batching is not significant. However for 3-cycles, batching can result in a considerable gain over greedy. Furthermore, using data from the National Kidney Registry, we provide simulations that confirm our theoretical results. 2 - Dynamic Matching In Over-the-counter Markets Yu An, Stanford, Stanford, CA, United States, yua@stanford.edu Zeyu Zheng We model the dynamics of liquidity premium in an OTC market with heterogeneous assets. A monopolistic dealer matches supply and demand flows in order to maximize his profits. Inventory building by the dealer increases the average waiting time for those customers who rejected immediacy offers, and therefore helps the dealer extract rents via liquidity premium. The dealer’s dual role of liquidity provision and matchmaking creates inefficient monopoly, and in equilibrium, he holds too much inventory compared to the first best. Our result helps explain the recent growth in all-to-all trading platforms in the corporate bonds market, as they circumvent these inefficiencies. 3 - Toward A Credit-based Mechanism For Dynamic Kidney Exchange John Dickerson, Carnegie Mellon University, dickerson@cs.cmu.edu, John Dickerson, University of Maryland, College Park, MD, 20742, United States, dickerson@cs.cmu.edu We discuss progress toward creating a credit-based matching mechanism for dynamic barter markets—-and kidney exchange in particular—-that is both strategy proof and efficient, that is, it guarantees truthful disclosure of donor- patient pairs from the transplant centers and results in the maximum global matching. We show that no such mechanism that supports cycles and chains of any length can be both long-term individually rational and economically efficient; we then give light assumptions under which such a mechanism can exist. Cambridge, MA, United States, mburq@mit.edu Vahideh Manshadi, Itai Ashlagi, Patrick Jaillet Empirical Research in Operations II Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: Nils Rudi, INSEAD, 1 Ayer Rajah Avenue, Singapore, 138676, Singapore, nils.rudi@insead.edu 1 - Fitting, Clustering And Forecasting Product Life Cycles: Model And Empirical Validation Jan A Van Mieghem, Harold Stuart Professor, Northwestern University, 1, Evanston, IL, 60209-2001, United States, We present an approach to fit product life cycle (PLC) curves from historical demand data and use them to predict/forecast demands of ready-to-launch new products. We propose three types of models to fit PLC: the BASS diffusion model, the polynomial model and the piecewise-linear model and compare their goodness-of-fit and complexity for fitting different categories of products. Using time-series clustering techniques, we cluster the fitted PLC curves into several representative patterns. Finally, we validate out-of-sample forecast accuracy using actual demand data of a computer company. vanmieghem@kellogg.northwestern.edu Kejia Hu, Jason Acimovic, Douglas Thomas MA27 201A-MCC

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