2016 INFORMS Annual Meeting Program

MD41

INFORMS Nashville – 2016

MD42 207D-MCC Pricing and Information Provision of Services Sponsored: Revenue Management & Pricing Sponsored Session Chair: Gad Allon, Northwestern University, Evanston, IL, United States, g-allon@kellogg.northwestern.edu 1 - Managing Customer Expectations And Priorities With Delay Announcements Gad Allon, Northwestern University, g-allon@kellogg.northwestern.edu, Achal Bassamboo, Qiuping Yu We study in a service environment, how to manage customers’ expectations and to prioritize customers appropriately to maximize the firm’s profits. Specifically, we focus on a setting where the firm uses only delay announcements and study the opportunities and limitations of this mechanism. We are particularly interested in when and how the customers can be influenced by delay announcements. 2 - Trading Time In A Congested Environment Luyi Yang, The University of Chicago, Chicago, IL, United States, luyi.yang@chicagobooth.edu, Laurens Debo, Varun Gupta We propose time trading mechanisms, in which customers who are privately informed about their waiting costs mutually agree on the ordering in the queue by trading positions. We design optimal mechanisms for the social planner, the service provider, and an intermediary who might mediate the trading platform. Both the social planner’s and the service provider’s optimal mechanisms involve a flat admission fee and an auction that implements strict priority. If a revenue- maximizing intermediary operates the trading platform, it should charge a trade participation fee and implement an auction with some restrictions on customer trade. 3 - Learning To Bid In Sequential Auctions With Budgets Without Market Information Yonatan Gur, Stanford University, ygur@stanford.edu, Santiago Balseiro We consider the of bidding in sequential auctions with budget constraints under incomplete information, where bidders do not know the valuation distributions and the budgets of their competitors. We present a general adaptive strategy consisting of an approximation scheme in the dual space, in which bidders adjust their multipliers (and accordingly, their bid functions) through the campaign according to their expenditures. When adopted by all bidders, we show that the long-run average payoffs of the strategy asymptotically converge to those under a fluid mean-field equilibrium. We also analyze off-equilibrium performance under arbitrary and utility maximizing deviations. 4 - Impact Of Uncertainty About Co-workers Capability On Server Behavior In Queueing Systems Masha Shunko, University of Washington, mshunko@gmail.com, Yaroslav Rosokha, Saurabh Bansal We study business environment in which multiple servers process individual orders, but receive payment based on the team performance. Specifically, we are interested in the case when there is uncertainty about each other’s capability, such as when teams are newly formed or when new members join the group. What impact does this uncertainty have on the productivity of workers? Do workers perform better or worse if they know what the co-workers are capable of? Can the productivity be manipulated (impacted) by provision of relevant information regarding others’ capability? We answer these questions using a behavioral lab experiment.

2 - Production Of Schooling John Ruggiero, University of Dayton, jruggiero1@udayton.edu In this paper we analyze the production of schooling using stochastic DEA. Using Banker’s stochastic DEA model, we estimate frontier production allowing for measurement error while controlling for the socioeconomic environment. We derive useful policy measures related to costs to help guide school funding. 3 - Insights From Machine Learning For Evaluating Production Function Estimators On Manufacturing Survey Data Andrew Johnson, Texas A & M University-College Station, ajohnson@tamu.edu, José Luis Preciado Arreola Organizations like census bureaus rely on non-exhaustive surveys to estimate industry population-level production functions. In this paper we propose selecting an estimator based on a weighting of its in-sample and predictive performance on actual application datasets. We compare Cobb-Douglas functional assumptions to nonparametric shape constrained estimators. For simulated data, we find that our proposed estimator has the lowest weighted errors. For actual data, specifically the 2010 Chilean Annual National Industrial Survey, a Cobb-Douglas specification describes at least 90% as much variance as the best alternative estimators in practically all cases considered. 4 - Endogenous Environmental Variables In Stochastic Frontier Models Artem Prokhorov, University of Sydney Business School, Sydney, Australia, artem.b.prokhorov@gmail.com, Christine Amsler, Peter Schmidt We consider an SFA model with errors e=v+uoexp(q’d), where v is normally distributed noise, uo generates technical inefficiency and q are the environmental variables that influence the level of technical inefficiency. In a previous paper we looked at the case that the input variables are correlated with v and u but q are exogenous. Here we allow q to be endogenous in the sense that they are not independent of v and/or uo. We consider estimation by IV and by MLE. MLE requires specification of reduced form equations. The case that q and uo are dependent is difficult because we need to assume a copula and so there are computational issues. MD41 207C-MCC Quantitative Methods for Finance and Energy Sponsored: Financial Services Sponsored Session Chair: Mendoza Rafael, rafamendoza1977@gmail.com 1 - Modeling Dependent Outages Of Electricity Generators Vishwakant Malladi, McCombs School of Business, vishwakant@gmail.com We present a framework where the electricity plants in a region are modeled as subordinated Markov Chains. We also develop a factor model for Markov chain generators to separate both the idiosyncratic and correlated behavior of the plants. Calibration shows that supply curves are bent resulting in lower generation capacity available at higher reliability levels. 2 - Dynamic Mean-variance Under Predictable Criteria Xiao Han, University of Texas-Austin, xhan581@gmail.com While mean-variance analysis has been widely adopted by investment professional as a basic asset allocation tool, its dynamic counterpart was rarely studied in academic literature. We aim to fill this gap by proposing a type of mean-variance criteria that is predictable, self-generating and consistent over time. The framework partially resolves the dilemma when searching for dynamic optimal portfolio strategies while facing uncertainties about model parameters and investment horizons. As an example, we solve the forward mean-variance problem when the investor only has partial information regarding the dynamics of asset returns. 3 - Static Hedging And Pricing Under The JDCEV Model Via Integral Equations Dong-Young Lim, Korea Advanced Institute of Science & Technology, DaeJeon, Korea, Republic of, ldy1848@kaist.ac.kr, Kyoung-Kuk Kim We provide a systematic approach to construct an exact static hedge for exotic options under the JDCEV model, using the theory of integral equations. We show the existence and uniqueness of an exact static hedging portfolio which consists of continuum of vanilla or binary options. Under suitable conditions, such a hedging portfolio can be explicitly found in terms of generalized hypergeometric functions. Also, we work on constructing a robust static hedge with finitely many hedging instruments, together with an efficient method of evaluating hedge errors. The effectiveness of the proposed method is demonstrated by several examples, including double barrier options and step up(down) options.

MD43 208A-MCC Simulation for Supply Chain Sponsored: Simulation Sponsored Session

Chair: Abdullah A Alabdulkarim, Dean, College of Engineering, Majmaah University, university campus, Majmaah, Majmaah, 1176, Saudi Arabia, aalabdulkarim2010@gmail.com 1 - Statistical Selection Of The Best Path In A Supply Chain David Goldsman, Georgia Institute of Technology, sman@gatech.edu We study statistical formulations of problems involving the selection of the best path from an origin to a destination through a network such as a supply chain. The term “best” can take a variety of meanings, e.g., the path having the (i) smallest expected travel time, (ii) highest probability of meeting a deadline, and

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