2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
160
MB35
205A-MCC
Empirical Research in Healthcare Operations
Sponsored: Manufacturing & Service Oper Mgmt, Service
Operations
Sponsored Session
Chair: Mor Armony, New York University, Stern School of Business,
New York, NY, 10012, United States,
1 - Refining Workload Measure In Hospital Units: From Census To
Acuity-adjusted Census In Intensive Care Units
Song-Hee Kim, Marshall School of Business, University of
Southern California, Los Angeles, CA, United States,
, Edieal Pinker, Joan Rimar,
Elizabeth Bradley
We aim to better understand the impact of ICU workload on patient outcomes, so
that practitioners and researchers can use such understanding to provide high
quality care despite increased hospital crowding. Using data from two ICUs and a
dynamic measure of patient acuity, we show when acuity-adjusted workload is
high sicker patients are discharged and longer-term outcomes are affected. Our
findings suggest 1) ICUs need to track changes in patient acuity and 2) future
studies of ICU workload should take patient acuity into account in workload
measures. Using a simulation study, we show how high acuity-adjusted workload
can be prevented by reducing seasonality in patient arrivals.
2 - Data-driven Appointment Scheduling Under Uncertainty:
The Case Of An Infusion Unit In An Oncology Center
Nikolaos Trichakis, MIT, Cambridge, MA, United States,
, Avishai Mandelbaum, Petar Momcilovic
We develop a novel, data-driven approach to deal with appointment sequencing
and scheduling in a multi-server system, where both customer punctuality and
service times are stochastic. Our approach relies on an infinite-server queuing
model approximation. We calibrate our model using a data set of unprecedented
resolution, gathered at a large-scale outpatient oncology practice, and illustrate
how our approach can be utilized to improve infusion scheduling. We also
demonstrate the performance of our approach by comparing it with existing state-
of-the-art sequencing and scheduling algorithms.
3 - The Effect Of Online Reviews On Physician Demand:
A Structural Model Of Patient Choice
Yuqian Xu, NYU, , Mor Armony,
Anindya Ghose
Social media platforms for healthcare services are changing how patients choose
doctors. In this paper, we wish to derive the impact of online information on
patient choice of outpatient care doctors. We are especially interested in how
operational factors influence demand. We propose a random coefficient logit
model to characterize consumer heterogeneity in doctor choices, taking into
account both numeric and textual user-generated content with text mining
techniques. Our interdisciplinary approach provides a framework that combines
machine learning and structural modeling techniques with empirical operations
management.
MB36
205B-MCC
Empirical and Theoretical Models in Supply Chains
Sponsored: Manufacturing & Service Oper Mgmt, Supply Chain
Sponsored Session
Chair: Anyan Qi, The University of Texas at Dallas, Business School,
Richardson, TX, 11111, United States,
Co-Chair: Ozge Sahin, Johns Hopkins University, 100 International
Drive, Baltimore, MD, 21202, United States,
1 - Assessing Uncertainty From Point Forecasts
Zhi Chen, INSEAD, Singapore, Singapore, ,
Anil Gaba, Dana Popescu
This paper develops a parsimonious model for combining correlated point
forecasts into a probability distribution for the quantity of interest. The model is
compared with other commonly used methods that either ignore the lack of
dependence between the point forecasts and/or use a certainty equivalent
approach in estimating the distribution parameters, hence ignoring the parameter
uncertainty. We further illustrate the implications for a decision maker in a
newsvendor setting, where our model leads to profits that are higher on average
when compared to the other widely used methods.
2 - Analytical And Empirical Study Of Complementarities In An Online
Advertising Supply Chain And Their Impact On Optimal Operating
Policies And Profits
Changseung Yoo, PhD Student,
The University of Texas at Austin, Austin, TX, United States,
Anitesh Barua,
Genaro Gutierrez
We examine channel structures and pricing models in an online advertising
supply chain using a proprietary dataset. We develop analytic as well as structural
econometric models that enable us to and quantify synergy effects between them.
While the extant literature emphasizes choosing between pricing models, we
show that using multiple models in concert yields higher overall profitability due
to strategic complementarities among the pricing schemes. We then explore the
operating implications of the complementarities and their impact on profits and
supply chain efficiency, and devise information/profit sharing contracts that boost
the supply chain profit towards the benchmark scenario.
3 - Supplier Centrality And Auditing Priority In Socially Responsible
Supply Chains
Jiayu Chen, The University of Texas at Dallas, Richardson, TX,
United States, , Anyan Qi,
Milind Dawande
We consider a supply network where buying firms’ brand may be damaged by
sourcing from suppliers who fail to comply with socially responsible standards. To
mitigate the risk, firms may audit their suppliers. We derive firms’ equilibrium
auditing strategy and propose approaches to mitigate the inefficiency.
4 - Dynamic Coordination In A Supply Chain With Production
Capacity Uncertainty
Zhongjie Ma, Purdue University, 403 W State Street, West
Lafayette, IN, 47906, United States, , Qi Feng,
J. George Shanthikumar
We study the effect of upstream supply capacity uncertainty on the inventory
decisions in a two-stage supply chain from both centralized and decentralized
perspectives. Extending the notion of stochastic linearity and directional concave
order, we show that the centralized problem is concave via transformation. This
observation allows us to extend the well-known Clark-Scarf decomposition
results to the multi-echelon inventory system with random capacity.
Furthermore, we discuss the mechanism to dynamically coordinate the supplier’s
and retailer’s decisions when they each possess private information.
MB37
205C-MCC
Socially and Environmentally Responsible
Operations Management
Sponsored: Manufacturing & Service Oper Mgmt,
Sustainable Operations
Sponsored Session
Chair: Michael Lim, U of Illinois at Urbana-Champaign,
Champaign, IL, United States,
Co-Chair: Karthik Murali, University of Alabama, Tuscaloosa,
Tuscaloosa, AL, United States,
1 - Optimal Feed-in Tariff Policies: The Role Of
Technology Manufacturers
Shadi Goodarzi, HEC, Paris, France,
Sam Aflaki, Andrea Masini
We assess the effectiveness of feed-in tariff policies in promoting renewable
energy technologies taking into account technology manufacturers’ decisions.
Modeling a three-tier supply chain that includes potential adopters, technology
manufacturers and a grid operator, we show that the ability of feed-in tariffs to
induce renewable energy adoption is strongly affected by the technology
manufacturers’ market characteristics.
2 - Product Allocation Under The Risk Of Recall
Long He, National University of Singapore, ,
Ying Rong, Zuo-Jun Max Shen
When product recalls happen, companies not only have to deal with additional
logistics costs but also a damaged reputation. To alleviate the severe consequences
of product recall, we develop a model to compare dedicated and uniform product
allocation strategies with associated sourcing plans. We also discuss the impacts of
key factors in the performance comparison.
MB35