2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
119
5 - Probability Analysis Of The Severity Of Train Derailments Using
Copula Models
Emmanuel Martey, University of Delaware, 302 DuPont Hall,
Newark, DE, 19716, United States, ,
Nii Attoh-Okine
In spite of their relatively low occurrence, train derailments have been a major
concern due to their high consequence. Derailment severity may depend on
various factors such as speed, accident cause and residual train length. It is
important to know the dependencies between these variables in order to better
understand how to reduce derailment severity. This paper presents the copula
approach as a technique for modeling dependencies between the various
variables. Copulas link arbitrary marginal distributions to form a joint
multivariate distribution with a particular dependence structure. Copulas are
suitable for modelling multivariate data with non-normality, tail dependency or
skewness.
SD72
Bass- Omni
Supply Chain Mgt IV
Contributed Session
Chair: Shabnam Rezapour, University of Oklahoma,
2248 Houston Ave. apt 2, Norman, OK, 73071, United States,
1 - Upstream Supplier And Downstream Customer Networks:
An Empirical Investigation
Marcus A Bellamy, Boston University, Rafik B. Hariri Building, 595
Commonwealth Avenue, Boston, MA, 02215, United States,
Soumen Ghosh, Manpreet Singh Hora
We examine the relationship dependence characteristics and structural
configuration of a firm’s supply chain as drivers of its performance using supply
chain relationship data from the Bloomberg database. We demonstrate how firm
performance may be influenced by the manner in which its cost is concentrated
upstream as a customer, its revenue is concentrated downstream as a supplier,
and its supply network is structured.
2 - Capacity Expansion Under Demand Uncertainty With
Uncertain Probabilities
Heejung Kim, University of California- Berkeley, Berkeley, CA,
94720-1777, United States,
Philip Kaminsky
Pharmaceutical industries make capacity investment decisions while clinical trials
for products are running. The demands are highly dependent on the test results,
and estimating exact probability distribution of the results is difficult. We focus on
developing and understanding capacity expansion models that are robust to any
possible probability distributions using multistage stochastic programming for
different objectives - minimizing expected cost, value at risk and conditional value
at risk.
3 - Supply Chain Partner Environmental Health And
Firm Performance
Marcus A Bellamy, Assistant Professor, Boston University, Rafik B.
Hariri Building, 595 Commonwealth Avenue, Boston, MA, 02215,
United States,
We empirically examine the relationship between the environmental initiatives
and outcomes of a firm’s supply chain partners and firm performance. We draw
from environmental, financial, and supply chain data to identify key mechanisms
related to the environmental health of a firm’s supply chain that influence its
economic performance.
4 - Component Procurement And End Product Assembly In An
Uncertain Supply And Demand Environment
Ramesh Bollapragada, San Francisco State University, School of
Business, 1600 Holloway Avenue, San Francisco, CA, 94132,
United States, , Saravanan Kuppusamy,
Uday S Rao
In this paper, we examine a multi-product, multi-component, procurement and
assembly problem with both supply and demand uncertainties. We explicitly
model the uncertainty using a stochastic program that facilitates procurement and
assembly decisions. We present a stochastic linear programming model of the
problem which we solve using its deterministic equivalent with a finite number of
scenarios. We identify the key cost drivers that need attention from managers in
the manufacturing industry, when there is limited knowledge of future demand
and component availability.
5 - Correlation Between Supply Networks’ Strategic And Operational
Risk Mitigation Strategies
Shabnam Rezapour, University of Oklahoma,
2248 Houston Ave. apt 2, Norman, OK, 73071, United States,
, Janet K. Allen, Farrokh Mistree
A supply network’s performance is affected by two types of uncertainty: 1)
disruptions distorting its topology; and 2) variations affecting its flow planning.
We show that strategic risk mitigation strategies, such as robustness and
resilience, and operational risk mitigation strategies, such as reliability,
neutralizing impacts of disruptions and variations respectively are correlated. A
model is developed to simultaneously determine robustness, resilience and
reliability. Our findings show that the correlation between: i) robustness and
resilience is negative; ii) robustness and reliability is positive; and iii) resilience
and reliability is negative.
SD79
Legends G- Omni
Health Care, Modeling IV
Contributed Session
Chair: Utpal Kumar Bhattacharya, Associate Professor, Indian Institute
of Management Indore, Pitampur Road, Prabandh Sikhar, Indore,
453556, India,
1 - Optimal Radiotherapy Treatment Policy Based On Tumor
Biological Response: A Partially Observable Markov Decision
Process Framework
Nasrin Nouri, PhD Student, University of Houston, 9701 Meyer
Forest Dr., Apt 6207, Houston, TX, 77096, United States,
In radiotherapy treatment planning the prescribed dose is delivered in equal
fractions of dose during 30 to 40 sessions to give healthy organs time to recover.
Depending on tumor state, the tumor growth and its response to radiation will
change, hence a dynamic treatment plan is required. It is not possible to observe
the tumor before each session through CT images so we are faced to uncertainty
of tumor state. In this study we develop a partially observable Markov decision
process to provide optimal treatment policy when the density of tumor is
uncertain. This approach provides the optimal policy determining when to choose
a less effective, less harmful dose over a more effective, more harmful dose.
2 - Reserving Walk-in Times In Primary Care
Brigitte Werners, Professor, Ruhr-University Bochum, Fac.
Management and Economics, Bochum, 44780, Germany,
For a primary care physician with varying workday demand, capacity reservation
for walk-ins and scheduled appointment slots is optimized on a tactical level.
Number and position of the scheduled appointments influence waiting times for
patients, capacity for treatment and the utilization of PCPs. A multi-criteria
mixed-integer linear programming model is suggested to find an acceptable
compromise solution. Results are evaluated by an extensive stochastic simulation
study.
3 - Econometric Model Of Critical Care Outreach Team And Intensive
Care Unit
Ali Haji Vahabzadeh, The University of Auckland Business School,
Private Bag 92019, Auckland, 1142, New Zealand,
Valery Pavlov
To analyse the role and functionality of the critical care outreach team (CCOT) in
hospitals, and particularly, its interactions with the ICU, we develop an
econometric model of CCOT and ICU. This allows us to estimate the impact of
CCOT intervention in detecting the critically ill patients in the ward on the ICU
length-of-stay, potential ICU readmission and patient outcome.
SD79