2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - The Analytics Of A Business Traveler’s Alert System
Fletcher Lu, Associate Professor, University of Ontario Institute of
Technology, Faculty of Business and IT, 2000 Simcoe Street North,
Oshawa, ON, L1H 7K4, Canada,
An insurance company is minimizing its business clients’ exposure to dangerous
and risky environments by providing a dynamic mobile travel alert system. I
present the analytics of this alert system, which gathers world-wide current
information using big data analytics to rank alerts regarding dangerous situations
and matches the alert information to clients’ relevant travel information such as
location, travel time, health issues, etc. to send to the clients. Included in the
system’s alerts are safety recommendations and instructions.
3 - Risk Sharing Between The Insured And The Insurer
Christopher Gaffney, Drexel University, 3141 Chestnut St,
LeBow College of Business, Philadelphia, PA, 19104, United States,
Risk reduction is a key benefit of insurance. We derive many of the properties
that govern the way in which risk is shared, with a particular focus on the
properties of insurer and insured variance and covariance. Additionally, we
consider the conception of optimal risk sharing, and discuss how an insurance
contract can constitute such an arrangement.
4 - Filtering For Risk Assessment Of Interbank Network
Majeed Simaan, PhD Student, RPI, 110 8th Street, Pittsburgh
Building, TROY, NY, 12180, United States, ,
Aparna Gupta, Koushik Kar
We develop a framework for risk assessment in an interbank network, where
banks interact with each other via short-term debt contracts. Focusing on the
demand side of liquidity and omitting credit rationing, the framework identifies
the interbank network structure and its degree of interconnectedness.
Identification is facilitated by a statistical learning procedure that reverse
engineers signals (transactions) observed in the interbank market and conducts
inference about banks’ individual and network-level characteristics. The results
from the simulation study undermine the value of integration, even when the
network is identified in its simplest forms.
5 - Decision Making Under Uncertainty Using Monte Carlo
Simulation: Case Of The Offshore Wind Industry Supply Chain
David Menachof, Peter Thompson Chair in Port Logistics, The
University of Hull, Logistics Institute, Hull University Business
School, Kingston upon Hull, HU6 7RX, United Kingdom,
, Negar Akbari
Offshore wind energy has emerged as an emission free source of energy globally,
especially across Northern European countries. The projects are now moving
further from shore and into deeper waters, which in turn increases the levelized
cost of energy (LCOE) and related risks. Furthermore, government support
schemes are also subject to uncertainties that influence investment in such
projects. A Monte Carlo Simulation model is proposed that aims to consider the
risks within the project and suggests ways that risks can be incorporated in the
project evaluation phase including port selection. An offshore wind project case is
considered and the results are reported.
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203B-MCC
Production and Scheduling II
Contributed Session
Chair: Jorge Pereira, Universidad Adolfo Ibáñez, Avda. Padre Hurtado
750, office 216-C, Viña del Mar, 2530852, Chile,
1 - Sequencing In Mixed Model Assembly Lines
Mary Beth Kurz, Clemson University, 271 Freeman Hall, Clemson,
SC, 29634-0920, United States, Anas Alghazi
Mixed model assembly lines are used by manufacturers to satisfy customer’s
demand for products’ customization while keeping the cost down. Since
customization is available via different options, some orders that include labor-
intensive options require more time to be assembled. In order to minimize work
overload throughout the assembly line, a sequencing decision must be taken to
sequence customer’s orders such that work overload is minimized. We investigate
this problem which is tackled in two different ways in the literature; the car
sequencing problem and the mixed model sequencing problem.
2 - A Systematic Literature Review Of Rolling Methods For
Production Planning
Reha Uzsoy, North Carolina State University, Dept. of Industrial &
Systems Engg, 300 Daniels Hall Camps Box 7906, Raleigh, NC,
27695-7906, United States, ,
Rafael Gumaraes Wollmann, Raimundo de Sampaio
The effective implementation of production plans over time requires them to be
updated a new information becomes available. This motivates the extensive use of
rolling methods in both industry and academia. Rolling methods for production
planning can be implemented in different ways and terminology is not always
well-defined in the literature. The objective of this paper is to characterize rolling
methods, focusing on the quality of the resulting production planning decisions,
using the Systematic Literature Review methodology and suggest unifying themes
and directions for future research.
3 - A Simulated Annealing Approach For Scheduling Jobs On
Identical Parallel Machines
Pravin Tambe, RCOEM, Katol Road, Nagpur, 440013, India,
Makarand Kulkarni
Identical parallel machine scheduling problem for minimizing the total tardiness is
a very important scheduling problem, but there have been many difficulties in
solving large size identical parallel machine scheduling problem with too many
jobs and machines. Metaheuristic approach like Simulated Annealing(SA) has
shown efficient results in solving the combinatorial optimization problem. In this
paper, a hybrid approach of a SA algorithm combined with backward-forward
heuristic has been proposed for solving identical machine scheduling problem for
minimizing the total tardiness. A numerical example of scheduling jobs on
identical high pressure die casting machines is presented.
4 - Cross-training Policies For Team Cost And Robustness
Jordi Olivella, Assistant professor, Universitat Politecnica de
Catalunya, Avda. Diagonal 647, Barcelona, 08028, Spain,
, David A Nembhard
We assess alternative cross-training policies for work-teams considering cost, and
levels of cross training. The policies are assessed with respect to their robustness
to demand-mix variation and absenteeism coverage. We employ simulation to
examine instances where cross training can be used to help meet a fixed demand
scenario, and with instances where cross-training can help to meet demand mix
variability. Current results indicate that when minimizing cross-training costs,
policies related to equalizing the cross-training level among the workforce, may
provide improvement in terms of robustness without additional cost.
5 - The Robust Simple Assembly Line Balancing Problem
Jorge Pereira, Universidad Adolfo Ibáñez, Avda.
Padre Hurtado 750, office 216-C, Viña del Mar, 2530852, Chile,
In this work we consider the simple assembly line balancing problem (SALBP)
with uncertainty on the operation time of the tasks. In order to deal with the
uncertainty, we put forward a robust version of the problem in which the
solution can handle a limited number of disruptions. Several new lower bounds
as well as adaptations of the current state-of-the-art procedures for the SALBP are
proposed. These methods are tested on a computational experiment, and the
results show that the method is able to solve large-sized instances within reduced
running times, outperforming available procedures in the literature.
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204-MCC
Operations Analysis in Healthcare
Sponsored: Manufacturing & Service Oper Mgmt,
Healthcare Operations
Sponsored Session
Chair: Tinglong Dai, Johns Hopkins University, Baltimore, MD,
United States,
Co-Chair: Daniel Ding, University of British Columbia, 2053 Main Mall,
Vancouver, V6T 1Z2, Canada,
1 - Allocation Of ICU Beds During Periods Of High Demand
Huiyin Ouyang, University of North Carolina,
We formulate an MDP model for admission decisions in an ICU where patients’
health conditions changeover time according to Markovian probabilities. We find
that the optimal decision can depend on the mix of patients in the ICU and
provide an analytical characterization of the optimal policy. We also identify
conditions under which the optimal policy is state-independent.
2 - Late-onset Neonatal Sepsis Prediction Using Supervised
Learning Techniques
Nadia Aly, College of William and Mary,
In this study, we derive features from the R-R intervals (distance between R
peaks) and apply novel machine learning algorithms to predict if an infant will be
diagnosed with a sepsis infection within the next twelve hours. The dataset used
in this study consisted of the R-R intervals recorded by monitoring electrodes in
the NICU for approximately 3,000 infants. A Support Vector Machine model,
outperformed all other models with a 0.07% false alarm rate and a 91.70%
classification accuracy. These encouraging results imply potential clinical
applications for the NICU to implement this algorithm on real-time heart rate
data to influence decisions on when to proceed with diagnostic procedures.
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