Informs 2015

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INFORMS Philadelphia – 2015
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SA42
SA42
42-Room 102B, CC
Joint Session HAS/MSOM-Health-Practice:
Operations Management of Emergency Services I
Sponsor: Health Applications/MSOM
Sponsored Session
Chair: Maria Mayorga, Associate Professor, University of North
Carolina, Dept. of Industrial & Systems Engineerin, Campus Box 7906,
Raleigh, NC, 27695-7906, United States of America,
1 - Two-stage Stochastic Programming to Redeploy and Dispatch
Ambulances with Restricted Workload
Shakiba Enayati, Research Assistant- PhD Candidate, North
carolina state university, NC State University 373 Daniels Hall,
Raleigh, NC, 27695-79, Osman Ozaltin,
Maria Mayorga
EMS system is responsible to dispatch ambulances to arriving emergency calls.
Redeployment strategy potentially improves the EMS performance as ambulances
becoming busy erratically. This study proposes a stochastic approach comprising
two steps. Each step is a two-stage stochastic programming in which the
redeployment occurs only for idle ambulances in the first stage. Dispatching
decisions are made in the second stage. Numerical results are provided based on
simulation for a large real dataset.
2 - Assessing the Impact of Flexible use of Observations Units
Gabriel Zayas-Caban, University of Michigan, 1205 Beal Avenue,
Ann Arbor, MI, 48109, United States of America,
We assess tradeoffs that result from flexible use of Observation Units (OUs). A
potential solution to reduce delays to care is to relocate boarding patients to an
OU, which offer an alternative to discharging or admitting ED patients by
allowing doctors to observe patients for an extended time. This allows ED beds to
become available, resulting in decreased times to first treatment for patients.
However, this has the potential to block the OU for patients needing observation.
3 - Dynamic Ambulance Management:Theory and Practice
Rob Van Der Mei, CWI, Kruislaan 123, Netherlands,
, Thije Van Barneveld, Sandjai Bhulai,
Martin van Buuren, Caroline Jagtenberg
Dynamic Ambulance Management (DAM) is a powerful means to reduce
response times for ambulance services, and the use of DAM is rapidly gaining
momentum. Over the past few years, we have developed a variety of DAM-
algorithms, each with their pros and cons. Recently, we have started a real-life
pilot to evaluate the different algorithms in practice. In this talk I will give an
overview of the algorithms developed, and discuss the lessons learned from the
DAM-pilot.
4 - Modeling Ambulance Dispatch Systems During Extreme
Weather Events
Eric Dubois, PhD Student, University of Wisconsin-Madison,
1513 University Avenue, Madison, WI, 53706,
United States of America, , Laura Mclay
Ambulance dispatch models traditionally focus on steady state systems operating
under normal conditions. We develop a Markov decision process to model the
system during extreme weather events where patient queueing and patient
health deterioration is relevant. We determine that under certain situations with
high ambulance utilization, average patient survival can be increased by
withholding ambulances from less serious patients in the expectation of more
emergent future calls.
SA43
43-Room 103A, CC
Revenue Management and Learning I
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: He Wang, MIT, 77 Mass Ave, E40-149, Cambridge, MA, 02139,
United States of America,
1 - Learning via External Sales Networks
Ankur Mani, NYU, 44 W 4th St, New York, NY, United States of
America, , Josh Reed, Ilan Lobel
We consider the problem of demand learning faced by a firm selling through an
external sales network. The firm is not able to control its product experimentation
and needs to rely on the decisions made by its sales agents. The only control
available to the firm is to remove products from its lineup. We show that if the
firm utilizes a well designed policy, it is able to obtain near-optimal
experimentation when the sales force is sufficiently large.
2 - Learning and Pricing using Thompson Sampling
He Wang, MIT, 77 Mass Ave, E40-149, Cambridge, MA, 02139,
United States of America, , Kris Johnson
Ferreira, David Simchi-levi
We consider a network revenue management problem where a retailer aims to
maximize revenue from multiple products with limited inventory. As common in
practice, the retailer does not know the expected demand at each price and must
learn the demand information from sales data. We propose an efficient and
effective dynamic pricing algorithm, which builds upon the Thompson sampling
algorithm used for multi-armed bandit problems by incorporating inventory
constraints into the pricing decisions.
3 - Nonparametric Self-adjusting Price Control
Stefanus Jasin, Stephen M. Ross School of Business, University of
Michigan, Ann Arbor, MI, United States of America,
, George Chen, Izak Duenyas
We consider dynamic pricing of multiple products with limited inventories. The
functional form of demand is not known. We devise a nonparametric heuristic
that consists of four elements: Spline approximation of the unknown demand
during the exploration stage, linear approximation of the estimated demand,
quadratic approximation of the estimated revenue, and self-adjusting control
during the exploitation stage. Our heuristic significantly improves the theoretical
bound of existing heuristics.
4 - Incomplete Learning and Certainty-equivalence Control
Bora Keskin, Duke University, Fuqua School of Business, 100
Fuqua Drive, Durham, NC, 27708-0120, United States of
America, Assaf Zeevi
Motivated by dynamic pricing applications, we consider a dynamic control and
estimation problem where a system manager sequentially chooses controls and
makes observations on a response variable that depends on chosen controls and
an unknown sensitivity parameter. The system manager uses a certainty-
equivalence decision rule to determine subsequent controls based on estimates,
and we characterize the asymptotic accuracy performance of this policy.
SA44
44-Room 103B, CC
Pricing and Consumer Behavior
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: Mikhail Nediak, Queen’s University, 143 Union Str., Kingston,
ON, K7L3N6, Canada,
Co-Chair: Yuri Levin, Queen’s School of Business, 143 Union St. West,
Kingston, Canada,
1 - A Non-parametric Approach to Dynamic Pricing with
Demand Learning
Guyves Achtari, Queen’s School of Business, 143 Union Str.,
Kingston, ON, Canada, , Mikhail Nediak
In many industries, firms have the capability of observing both sales and the
refusal to buy from their customers. In situations where demand is unknown,
firms may use early sales data to forecast demand. We consider a situation where
the firm does not know demand, but can observe arriving customers refuse or
accept to buy a product at a given price. We formulate a dynamic program which
aims to dynamically adjust the price of the product in order to maximize the
firm’s total expected revenue.
2 - Turnpike Equilibrium for Oligopolistic Dynamic Pricing
Competition with Strategic Consumers
Jue Wang, Post-doctoral Fellow, Queen’s School of Business,
143 Union St. West, Kingston, ON, K7L 3N6, Canada,
, Yuri Levin, Mikhail Nediak
We consider the oligopolistic price competition when the prices are dynamic and
the customers are strategic. We formulate the problem as a fluid model in the
optimal control framework, and show that the equilibrium has a turnpike
property. We characterize the structure of turnpike for symmetric oligopoly and
asymmetric duopoly. The impact of non-stationary demand is also examined.
3 - Quantity Competition in a Multi-product Exchange Market with
Strategic Consumers and Dynamic Preference
Samuel Kirshner, UNSW Business School, Kensington, NSW,
Australia, , Mikhail Nediak,
Yuri Levin
We study a general multi-product multi-period exchange market for gross
substitute products. Consumers maximize surplus under the uncertainty of their
future product preferences deciding the quantity of products to purchase and sell
in each period. Under mild assumptions, the equilibrium trades and price path is
unique. The model is used to explore how strategic behavior of consumers and
preference dynamics impact the equilibrium and aggregate welfare.