2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
159
MB32
203A-MCC
Structural Estimation in Operations
Sponsored: Manufacturing & Service Oper Mgmt
Sponsored Session
Chair: Gabriel Weintraub, Columbia University, New York, NY,
United States,
1 - The Efficacy Of Incentives In Scaling Up Marketplaces
Ashish Kabra, INSEAD, Boulevard de constance, Fontainebleau,
77305, France, Elena Belavina,
Karan Girotra
Marketplaces spend billions in incentives to achieve scale, which is key to the
efficacy and survival of marketplaces. Using detailed transaction data from a
leading transportation marketplace, we estimate and compare the effects of
incentives given to the “buyer” side and “seller” side of the marketplace as well as
the effect of threshold and linear incentives.
2 - Spatial Competition And Preemptive Entry In The Discount
Retail Industry
Fanyin Zheng, Columbia Business School,
This paper studies how discount retailers make store location decisions by
estimating a dynamic game model. It extends the empirical models of dynamic
oligopoly entry by allowing for spatially interdependent entry and introducing
machine learning tools to infer market divisions from data. The results suggest
that preemptive incentives are important in chain stores’ location decisions and
that they lead to loss of production efficiency.
3 - Ergodicity And The Estimation Of Markov Decision Processes
Robert Bray, Kellogg,
I create a class of dynamic discrete choice estimators that exploit Markov chain
ergodicity. The empirical likelihood of a Markov decision process depends only on
the differences in the value function. And whereas the value function converges
with Bellman contractions at the rate of cash flow discounting, the value function
differences converge at the rate of cash flow discounting times the rate of Markov
chain mixing (the subdominant eigenvalue of the state transition matrix). With
this strong convergence result, I make Rust’s (1987) nested fixed point (NFXP)
estimator 200 times faster in problems with more than 2,000 states.
4 - When Demand Projections Are Too Optimistic: A Structural Model
Of Product Line Decisions
Andres I Musalem, U. de Chile / Complex Engineering Systems
Institute, Beauchef 851, Santiago, 8370456, Chile,
A methodology is proposed to estimate structural models of product line
competition. Not accounting for this endogeneity leads to overoptimistic estimates
of demand due to a sample selection bias, which may generate misleading
managerial recommendations. The methodology is illustrated using simulated and
real data.
MB33
203B-MCC
Simulation II
Contributed Session
Chair: Li Li, Southwest Jiaotong University, No.111, North Section
Second Ring Road, Chengdu, China,
1 - Operationalizing Industry Cluster Strategies
Tayo Fabusuyi, Numeritics & Carnegie Mellon University, 5520
Baywood Street, Floor #3, Pittsburgh, PA, 15206, United States,
Juergen Pfeffer
Local economic development organizations are often tasked with promoting the
health of the regional economy. However, the unique composition of each
geographical area calls for a distinct approach that reflects the peculiarities of the
local economy. We present an approach by which the information in input-
output is modeled and enriched using network analysis. Using a simulated policy
intervention, we show how the approach can provide insight on regional
economies and provide an application to industry cluster analysis.
2 - A Comparison Of Gaussian Process Modeling Software
Collin Erickson, Northwestern University, 2145 Sheridan Road,
Room C210, Evanston, IL, 60208, United States,
, Bruce Ankenman,
Susan M Sanchez
We have found that different software packages can give different results when
fitting the same Gaussian process model, often called kriging. We compare various
packages on a variety of test problems, finding that the accuracy of predictions
can differ significantly. An attractive feature of Gaussian process fitting is that the
model includes an estimate of predictive variance. We focus on evaluating the
reliability of this predictive error from these various packages. When fitting the
same data and model, the run times of certain packages can also differ by orders
of magnitude. The study takes a practitioners point of view, using each package
with minimal tuning.
3 - Time Management Policies In A Queueing System
Ji-Eun Kim, PhD Student, The Pennsylvania State University,
Imperial Towers, University Park, PA, 16801, United States,
David A. Nembhard, Hyeong Suk Na
Many job assignment problems are organized from a company’s perspective to
meet the demands of a schedule or to maximize workers’ productivity, often
ignoring the heterogeneity of pacing styles among workers. We show that if one
considers the diversity in pacing styles, system productivity can be increased using
one or more approaches. The purpose of this study is to test job assignment
policies to be used in a queueing system considering servers’ diversity in deadline
reactivity. Empirical course website data was used to test a range of job
assignment policies.
4 - Coordinating Station And Network Capacity In Urban Rail
Transit System
Li Li, Southwest Jiaotong University, No.111,
North Section 1, Second Ring Road, Chengdu, China,
, Haifeng Yan, Gongyuan Lu,
Wu You
The performance of urban rail transportation is impacted by fluctuated passenger
demand due to both the capacity constraint of station and line. The feature of
high accessibility and volume makes a well coordinated train line plan in urban
rail network very hard to be achieved. This research will present a stochastic
integer programming model to demonstrate the mutual influence between
passenger demand and train line plan. This model is solved by a simulation based
approach which is applied in a real-world case in Chongqing Rail Transit
Company.
MB34
204-MCC
Simulation and Stochastic Optimization
Sponsored: Manufacturing & Service Oper Mgmt, Healthcare
Operations
Sponsored Session
Chair: Douglas Morrice, University of Texas-Austin, 2110 Speedway
Stop B6500, Austin, TX, 78712-1750, United States,
1 - Multimodularity In The Stochastic Appointment Scheduling
Problem With Discrete Arrival Epochs
Christos Zacharias, Assistant Professor, University of Miami, Coral
Gables, FL, United States, , Tallys Yunes
We address the problem of designing appointment scheduling strategies that
account for patients’ no-show behavior, non-punctuality, emergency walk-ins
and random service times. We maintain the discrete nature of the appointment
scheduling problem by considering arrival epochs with discrete supports. We
demonstrate that the optimal scheduling strategy minimizes a multimodular
function, and a local search algorithm terminates with a globally optimal solution.
2 - Appointment Scheduling With Multiple Providers And Stochastic
Service Times
Michele Samorani, Santa Clara University, Santa Clara, CA, United
States, S Abolfazl Soltani, Bora Kolfal
We consider a multi-server appointment scheduling problem in which patients
may not show up, and those who show up require stochastic service times. We
model this problem as a Markov Chain and solve it through complete
enumeration. Then, we employ statistical learning techniques to detect patterns
among optimal solutions. We develop an effective heuristic method which uses
these patterns to build near-optimal solutions. Our numerical experiments show
that our methods result in higher-quality schedules than those obtained by
existing models. We also test our heuristic with a field experiment made in
collaboration with a local legal counseling clinic afflicted by high service time
variability.
3 - Coordinated Appointment Scheduling Of An Integrated
Practice Unit
Douglas Morrice, The University of Texas, Austin,
Dongyang Ester Wang,
Kumar Muthuraman, Jonathan F Bard, Luci Leykum,
Susan Noorily
In this research, we develop a coordinated approach to patient appointment
scheduling that enables a patient to receive multiple services on a single visit. The
approach is compared to heuristics used in practice. A case study in pre-operative
care involving the integration of Anesthesiology and Internal Medicine is used to
motivate and illustrate the results.
MB34