2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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203B-MCC
Production and Scheduling I
Contributed Session
Chair: Atieh Madani, University at Buffalo, The State University of
New York, 11221 Nickel Way, Amherst, NY, 14228, United States,
1 - Transient Analysis Of Geometric Serial Lines With Perishable
Intermediate Products
Feng Ju, Assistant Professor, Arizona State University, 699 S. Mill
Avenue, #553, Tempe, AZ, 85287, United States,
Ningxuan Kang, Li Zheng
Perishable intermediate products are commonly seen in practical manufacturing
systems, where the residence time of intermediate products in the buffer is
limited. Parts have to be scrapped if their maximum allowable time is exceeded.
In order to reduce the scrap ratio and optimize the production operation in a
timely manner, the transient behavior of the system needs to be investigated to
evaluate and predict the system performance in real time. In this paper, we
develop an analytical model to analyze the transient behaviors for such systems.
Compared with simulation, it is shown that the proposed method could estimate
the system’s transient performance with high accuracy.
2 - Integrated Production Planning And Distribution Problems
Utku Koc, MEF University, Huzur Mah. Ayazaga Cad No:4,
Maslak-Sariyer-Istanbul, Istanbul, 34396, Turkey,
In this study, we consider multiple integrated production and distribution
problems. Given a set of orders, a subset will be selected to be included in the
production plan. Each order has a profit, size and deadline for distribution.
Among the two types of vehicles available for distribution, the first type is
unlimited in supply but costly. The availability of the second type of vehicle,
which is less costly, varies by time. A number of problem classes are defined
depending on the distribution characteristics of the system. The difficulty of each
class is determined. Moreover, the value of integration is assessed depending on
the distribution characteristics.
3 - Incorporation Of Breaks In A Staffing Model For A Service Center
With Flexible Servers
Atieh Madani, University at Buffalo, The State University of New
York, 11221 Nickel Way, Amherst, NY, 14228, United States,
The shift scheduling problem (SSP) is a problem of assigning tasks to the
resources for each shift with the aim of minimizing costs and fulfilling the
demand. In our study we consider breaks, different levels of skill (for staff), and
multiple tasks for resources in the shift scheduling problem. In our work we use
the shift scheduling model that is developed by Batta, Berman, and Wang as the
base model. A column generation heuristic is developed and used to solve this
problem. The performance of this model and heuristic method is good. The
average gap between the integer program result and the lower bound (for
different size of problems) is 2.47%.
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204-MCC
Operational and Economic Models in Healthcare
Sponsored: Manufacturing & Service Oper Mgmt,
Healthcare Operations
Sponsored Session
Chair: Huiyin Ouyang, University of North Carolina, Hanes Hall CB#
3260, Chapel Hill, NC, 27599, United States,
Co-Chair: Serhan Ziya, University of North Carolina, 356 Hanes Hall
CB# 3260, Chapel Hill, NC, 27599, United States,
1 - Managing Returning Customers In An Appointment Based
Service System
Yichuan Ding, University of British Columbia,
We study how to manage returning customers in a slotted queue with the goal of
maximizing service volume. Applications of this model include outpatient or
dental appointment scheduling. We consider a simple strategy that a service
provider may use to reduce balking — book a potential returning customer right
before she leaves the clinic. We focus on a threshold-type policy and prove that
the throughput rate is a quasi-concave function of the threshold under the retrials
see time averages (RTA) assumption. We also analyze possible impact of the panel
size and panel mix on the choice of this threshold.
2 - Proactive Patient Service: An Operational And Economic Analysis
Kraig Delana, London Business School,
Nicos Savva, Tolga Tezcan
We present an operational and economic analysis of the value derived from
healthcare providers using knowledge of upcoming patient care needs to
proactively initiate service. On the operational side, we develop a novel queueing
model to show that proactive service leads to significant reduction in delays. On
the economic side, we show that if proactive service generates an inconvenience
cost, strategic patients are less likely to choose to adopt than socially optimal due
to economic frictions. This has important implications for providers working to
implement proactive service in practice.
3 - Online Surgical Case Scheduling With Repeating Blocks
Shashank Goyal, University of Minnesota, Minneapolis, MN,
United States, , Diwakar Gupta
Hospitals often assign surgeons a fixed number of blocks of operating-room time
on a periodic basis. The value and the duration of a surgery request are revealed
upon its arrival. The surgeon must place it in one of the blocks or decline it
without knowing the future requests. This decision must respect capacity
constraints and cannot be modified at a later time because the patients have to
make arrangements for travel and post-operative care. The aim is to maximize the
total value of accepted requests. We model the problem as the online multiple
knapsack problem and propose algorithms that have provable worst-case
performance. We also present bounds on the best performance that any algorithm
can achieve.
4 - Data-driven Patient Scheduling In Emergency Departments:
A Hybrid Robust-stochastic Approach
Meilin Zhang, National University of Singapore,
Emergency care necessitates adequate and timely treatment, which has
unfortunately been compromised by crowding in many emergency departments
(EDs). To address this issue, we study patient scheduling in EDs so that
mandatory targets imposed on each patient’s door-to-provider time and length of
stay can be collectively met with the largest probability. Exploiting patient flow
data from the ED, we propose a hybrid robust-stochastic approach to formulating
the patient scheduling problem, which allows for practical features such as a
time-varying patient arrival process, general consultation time distributions, and
multiple heterogeneous physicians.
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205A-MCC
Topics in Service Operations
Sponsored: Manufacturing & Service Oper Mgmt, Service
Operations
Sponsored Session
Chair: Yong-Pin Zhou, University of Washington, Seattle, Michael G.
Foster School of Business, Seattle, WA, 98195, United States,
1 - Search Among Queues Under Quality Differentiation
Luyi Yang, University of Chicago, Chicago, IL, United States,
Laurens G Debo, Varun Gupta
To understand implications of policy initiatives to cut elective surgical wait times,
we build an equilibrium search model where customers choose over a large
collection of vertically differentiated, congested service providers. We find that
policies that reduce either the search cost or customer demand may increase the
average waiting time in the system as customers substitute toward high-quality
service providers. Moreover, the improved quality customers obtain may not
compensate the prolonged wait, degrading the overall search reward while
yielding no returns in customer welfare.
2 - Observational Learning In Environment With Multiple Options
Chen Jin, Northwestern University,
This paper studies, both theoretically and empirically, the choice strategy
(behavior) of agents in a system with multiple options for which agents observe
the aggregate choices of previous agents. With information asymmetry regarding
the quality of the different options, the choices of better informed agents turn
sales into informative signals, allowing uninformed agents to learn about the
options’ quality. Contrary to the traditional observational learning literature with
binary choice, our theoretical analysis shows that options with less sales usually
have higher chance of being high quality. But the experiment data reveals that
uninformed subjects tend to choose options with most sales.
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