2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Facility Recovery Plan Under Resource Constraints
Gang Li, Bentley University, Management Department, 175 Forest
Street, Waltham, MA, 02452-4705, United States,
Xiangtong Qi
We address a facility recovery plan problem after disruption. The disruption has
damaged some of the existing facilities while the resources used to repair these
facilities are limited. This plan entails supply adjustment among customers and
resource allocation among damaged facilities in order to minimize the total supply
and shortage cost during the recovery period. In this talk, we present the problem
formulation and discuss efficient algorithms to solve.
4 - Energy Conscious Robot Scheduling In Robotic
Cell Manufacturing
Vahid Eghbal Akhlaghi, Research Assistant, Middle East Technical
University, Cankaya, METU, Yurt. 2, room 112-1, Ankara, 06800,
Turkey, , Hakan Gultekin, Sinan Gurel
Robotic cells usually operate under time pressure to minimize time related
objectives such as cycle time. Besides, robots consume significant amount of
energy determined by the speeds and distances of their moves. We study the
tradeoff between cycle time and energy consumption of a robot in a two machine
flexible robotic cell. There are alternative cyclic schedules for such a cell and each
cycle involves a number of different robot moves. Energy consumption of a robot
is formulated as a convex function of its speed. Given a cycle time, we find
optimal speeds for different robot moves in robotic cell cycles. We determine the
best cyclic schedule and optimal robot speeds that minimize energy consumption.
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203B-MCC
Simulation II
Contributed Session
Chair: Geonsik Yu, Yonsei University, Seoul, Korea, Republic of,
1 - Siting A Geological Disposal Facility – A Discrete Event
Simulation Approach.
Matthew Gilbert, Mr, University of Warwick, Statistics, University
of Warwick, Coventry, CV4 7ES, United Kingdom,
, Simon French, Jim Q Smith
Disposal of nuclear waste has become an increasing concern for the UK
government over the past few decades. We present a discrete event simulation
modelling changes in public opinion for their previous failed geological disposal
siting attempt in Cumbria between 2009 and 2013. Using this we explore
potential bias that could have been introduced at the end of the process.
2 - Estimating Cumulative Mean Behavior Using Standardized
Time Series
Dashi Singham, Naval Postgraduate School, 1411 Cunningham
Road, Operations Research Department, Monterey, CA, 93943,
United States, , Michael Atkinson
We present an alternative to confidence intervals that evaluates the probability a
sequentially updated sample mean stays within a given distance from the true
mean of a process after a given initial sample size. This measure of reliability relies
on properties of standardized time series, which we explore in relation to
Brownian bridges.
3 - Managing The Delays Caused By Slow Groups In Golf
MoonSoo Choi, Columbia University, 500 West 120th Street, S.W.
Mudd Building, New York, NY, 10027, United States,
Qi Fu, Ward Whitt
We use simulation to study various models of slow groups and their significant
impact on the pace of play in golf. We also compare different remedies to reduce
the delays caused by slow groups. The golf course model is a series of eighteen
queues in which multiple groups can play simultaneously with random stage
playing times and precedence constraints.
4 - Simulation Metamodeling In The Presence Of Model Inadequacy
Xiaowei Zhang, Hong Kong University of Science and Technology-
HKUST, Room 5559B, Academic Building, HKUST, Clear Water
Bay, Hong Kong, NA, China, , Lu Zou
A simulation model is often used as a proxy for the real system of interest in a
decision-making process. However, no simulation model is totally representative
of the reality. The impact of the model inadequacy on the prediction of system
performance should be carefully assessed. We propose a new metamodeling
approach to simultaneously characterize both the simulation model and its model
inadequacy. Our approach utilizes both simulation outputs and real data to
predict system performance, and accounts for four types of uncertainty that arise
from unknown performance measure of the simulation model, simulation errors,
unknown model inadequacy, and observation errors of the real system.
5 - When The Diversified Organization Prevails:
A Simulation Approach
Geonsik Yu, Yonsei University, Seoul, Korea, Republic of,
This research investigates through simulation the mechanism under which
diversity improves the performance of a company. In experiments, we focus on
the settings where the organization consisting of dissimilar members and try to
explain the reported reality-instances where diversity is ineffective. Analyzing the
characteristics of those settings, we identify that under what conditions the
organizational diversity is more or less effective. The simulation model newly
formulated in this study extends the existing methods that use discrete vectors
and genetic algorithm and is able to include market needs and product paradigm
shifts.
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204-MCC
Joint Session HAS/MSOM: Healthcare Operations
Sponsored: Manufacturing & Service Oper Mgmt,
Healthcare Operations
Sponsored Session
Chair: Mahboubeh Madadi, Louisiana Tech University,
123, Ruston, LA, 71272, United States,
1 - Team Primary Care Practice Scheduling Problem
Ekin Koker, University of Massachusetts-Amherst, Amherst, MA,
United States, , Hyun Jung Oh,
Hari Balasubramanian, Ana Muriel
We consider the team primary care practice scheduling problem where each
patient is seen by one of many available nurses before seeing her provider. In
other words, the nurse step is flexible while the provider step is dedicated. Both
steps have uncertain durations and these durations further depend on the type of
patient — some patients require a longer service duration than others. The
patients can also crossover in schedule, so the order of patients seen by the nurse
might not be the same as the order in which the provider sees patients. We
develop a two-stage stochastic integer programming model to solve this
challenging scheduling problem and present computational results.
2 - Optimal Decision Making In The Processing Of Human
Breast Milk
Ruichen Sun, University of Pittsburgh, Pittsburgh, PA, United
States, , Lisa M Maillart, Andrew J Schaefer
Donated breast milk - collected, processed and dispensed via milk banks - is the
standard of care for premature and unhealthy infants whose mothers cannot
provide adequate supply. We formulate a multi-criteria mixed-integer program to
optimize the daily decisions involved in the pooling of milk from different donors
to meet macronutrient requirements across different product types, and the
batching of pooled milk for efficient pasteurization. Our numerical results
demonstrate significant improvements compared to historical decisions at our
partner milk bank.
3 - A Stochastic Programming Approach For Surgery Scheduling
Under Limited Availability Of Turnover Teams
Serhat Gul, TED University,
The number and availability of turnover teams may significantly affect the
performance of a surgery schedule. We propose a two-stage stochastic integer
programming formulation for setting the patient appointment times under limited
availability of turnover teams. We consider the duration of surgical operation and
turnover to be random variables. The objective is to minimize the competing
criteria of patient waiting time and operating room idle time. We present a
heuristic to solve the problem. We conduct numerical experiments using data
from a large hospital.
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