A Schema Matching Architecture for the
Bioinformatics Domain
Dagmar Köhn (University of Rostock) and Lena
Strömbäck (Linköpings Universitet)
Abstract:
One of the main goals in bioinformatics research today
is to understand how various organisms function as biological systems. In
order to find this out, one must understand the reactions taking place within
the organism going down to interactions within molecules. Here, integration of
data from various sources are important and various standards for
representation are available, e.g., SBML, PSI MI, and BioPAX. This means there
is a need for transformations of those standards into each other. The common
representation formats for standards within the area are XML or OWL and a way
of mapping them would be of high interest for system biology researchers. In
this abstract we propose a solution for the mentioned problems and introduce a
possible future architecture for this solution.
Assessment of Transport Appraisal by the Use of
Monte Carlo Simulation: The CBA-DK Model
Kim Bang Salling and Steen
Leleur (Centre for Traffic and Transport - Technical University of Denmark)
Abstract:
This paper concerns a newly developed software model
called CBA-DK for project evaluation in the Danish road sector. CBA-DK is
developed as a combined effort in co-operation between the Danish Road
Directorate and the Technical University of Denmark. The main purpose of this
paper is primarily to describe how to implement a Monte Carlo Simulation in
CBA-DK by use of a software system named @RISK. First the two main modules of
CBA-DK are described as respectively a traditional cost-benefit analysis
(deterministic point estimate) and a risk analysis using Monte Carlo
Simulation (stochastic interval estimate). Next the actual case example is
presented with the obtained results. Finally, conclusions and a perspective of
the future modeling work are given.
Combining Lean Thinking and Computer
Simulation in Healthcare Delivery
Luciano Brandao de Souza
(Lancaster University)
Abstract:
In many countries there is an increasing concern about
the explosion of healthcare costs, without an equivalent improvement in
healthcare delivery being observed. As a consequence, finding solutions for
this problem is a current debate. This research project proposes improving
healthcare systems by applying a combined approach using discrete event
simulation (DES) and lean thinking. On the one hand, DES is a proved useful
technique for capacity models. On the other hand, lean thinking, a philosophy
focusing on reduction of wasteful processes, has achieved remarkable results
in manufacturing, but its applicability and usefulness in healthcare are still
under discussion. We suggest that the use of DES capacity models can
successfully address crucial problems to lean healthcare implementation, such
as patients’ security and practitioners’ commitment. For this reason this
research also investigates how computer simulation can support the
implementation of lean thinking in healthcare systems.
A Simulation Analysis of Multicasting in Delay
Tolerant Networks
Muhammad Abdulla (George Mason University)
Abstract:
Delay tolerant networks (DTNs) are a class of systems
that experience frequent and long-duration partitions. As in all distributed
systems, DTN multicasting is a desirable feature for applications where some
form of group communication is needed. The topological impairments experienced
within a DTN pose unique challenges for designing effective DTN multicasting
protocols. In this paper, we examine multicasting in DTNs. Unlike earlier work
we assume no knowledge of node connectivity or mobility patterns. We propose
the use of both single-copy and multi-copy routing DTN routing algorithms. We
also explore the use of gossiping and core nodes in DTNs to decrease the
number of redundant messages while maintaining high message delivery ratios.
We have performed extensive evaluations of our proposed methods. Our results
show that with careful protocol parameter selection it is possible to achieve
high delivery rates for various system scenarios.
Yield Curve Scenario Generation for Liquid
Asset Portfolio Optimization
Helgard Raubenheimer and Machiel F.
Kruger (North-West University (Potchefstroom Campus))
Abstract:
Maintaining liquid asset portfolios involves a high
carry cost and are mandatory by law for most financial institutions. Taking
this into account a financial institution's aim is to manage a liquid asset
portfolio in an "optimal" way, such that it keeps the minimum allowed liquid
assets to comply with regulations, whilst maximizing the portfolio return to
cover at least the carry cost. Stochastic Programming is nowadays applied to a
wide range of portfolio management problems similar to ours. The most
important step in the multi-staged stochastic programming approach is
generating a scenario tree which represents the uncertainty in the evolution
of risk factors over time. The scenario tree is a discrete approximation of
the joint distribution of these random factors. By using moment matching
techniques we construct scenario trees with discrete yield curve outcomes
sufficient for the pricing of liquid assets.
On the Performance of Inter-Organizational Design
Optimization Systems
Paolo Vercesi (Esteco) and Alberto Bartoli
(DEEI)
Abstract:
Simulation-based design optimization is a key
technology in many industrial sectors. Recent developments in software
technology have opened a novel range of possibilities in this area. It has now
become possible to involve multiple organizations in the simulation of a
candidate design, by composing their respective simulation modules on the
Internet. Thus, it is possible to deploy an inter-organizational design
optimization system, which may be particularly appealing because modern
engineering products are assembled out of smaller blocks developed by
different organizations.
Applications of Discrete-Event Simulation to
Support Manufacturing Logistics Decision-Making: A Survey
Marco
Semini (NTNU)
Abstract:
This paper presents a literature survey on recent use
of discrete-event simulation in real-world manufacturing logistics
decision-making. The sample of the survey consists of 52 relevant application
papers from recent Winter Simulation Conference proceedings. We investigated
what decisions were supported by the applications, case company
characteristics, some methodological issues, and the software tools used. We
found that most applications have been reported in production plant design and
in the evaluation of production policies, lot sizes, WIP levels and production
plans/schedules. Findings also suggest that general-purpose DES software tools
are suitable in most of these cases. For different possible reasons, few
applications for multi-echelon supply chain decision-making have been
reported. Software requirements for supply chain simulations also seem to
differ slightly from those for established application areas. The applications
described were carried out in a variety of different industries, with a clear
predominance in the semiconductor and automotive industries.
Properties of Q-Statistic Monitoring
Schemes
Paul Zantek and Scott T. Nestler (University of Maryland)
Abstract:
The Q-Shewhart scheme for detecting process mean shifts
is useful when the process parameters are unknown, as occurs in lean
operations and processes characterized by rapid innovation. The scheme is
known to have an early-detection advantage over other monitoring schemes,
which is a desirable property in cases where it is necessary to react very
quickly (such as bioterrorist attacks). Computing the distribution of the time
until the Q-Shewhart scheme detects a shift involves the evaluation of
high-dimensional integrals that do not have known closed-form solutions. In
lieu of quadrature, which is computationally expensive, we propose to compute
the integrals via a Monte Carlo integration procedure that incorporates
importance sampling. The proposed computational procedure is validated by
comparing the results of the RL distribution with those from direct simulation
(Q-Shewhart scheme applied to simulated process observations). The procedure
provides, on average, a 48% savings in CPU time over direct simulation.
In Silico Modeling of Drug Transport Across
Biological Barriers
Tai Ning Lam (University of California, San
Francisco, School of Pharmacy), Lana Garmire (University of California,
Berkeley) and C. Anthony Hunt (University of California, San Francisco)
Abstract:
We constructed an object and aspect oriented model to
represent drug permeation across biological barriers. We assembled software
components in a way that represents biological mechanisms. Simulation outputs
mimic measurements made of traditional wet-lab observations. The model is
intended for experimentation and to further explore pharmacokinetic processes.
We report simulation results that are consistent with traditional models.
Designing A Simulation Model Of The 2011
Census
Simon Doherty (University of Southampton)
Abstract:
The aim of the doctorate project is to develop a
simulation model that will assist the Office for National Statistics (ONS)
with planning the 2011 Census in the UK. The model will replicate the field
operations surrounding the Census. These are principally the delivery of the
questionnaire to each household, the initial flow of responses back, and then
the follow up of households that have not returned a form. For each stage, ONS
has different strategic options available, each of which will affect the cost
of the operation and the Census response rate. The model will be used in
conjunction with optimisation techniques to try and find the best combination
of strategies in order to maximise the response rate whilst staying within the
overall budget of the project.
Adaptation of the UOBYQA Algorithm for Noisy
Functions
Geng Deng and Michael C. Ferris (University of
Wisconsin-Madison)
Abstract:
In many real-world optimization problems, the objective
function may come from a simulation evaluation so that it is (a) subject to
various levels of noise, (b) not differentiable, and (c) computationally hard
to evaluate. In this paper, we modify Powell's UOBYQA algorithm to handle
those real-world simulation problems. Our modifications apply Bayesian
techniques to guide appropriate sampling strategies to estimate the objective
function. We aim to make the underlying UOBYQA algorithm proceed efficiently
while simultaneously controlling the amount of computational effort.
Cycle-Time Quantile Estimation in Manufacturing
Settings Employing Non-FIFO Dispatching Policies
Jennifer McNeill
Bekki, John W. Fowler, and Gerald T. Mackulak (Arizona State University)
Abstract:
Previous work has shown that the Cornish-Fisher
expansion (CFE) can be used successfully in conjunction with discrete event
simulation models of manufacturing systems to estimate cycle-time quantiles.
However, the accuracy of the approach degrades when non-FIFO dispatching rules
are employed for at least one workstation. This paper suggests a modification
to the CFE-only approach which utilizes a power data transformation in
conjunction with the CFE. An overview of the suggested approach is given, and
results of the implemented approach are presented for a variety of models
ranging in complexity from simple queueing models to a model of a non-volatile
memory factory. Cycle-time quantiles for these systems are estimated using the
CFE with and without the data transformation, and results show a significant
accuracy improvement in cycle-time quantile estimation when the transformation
is used. Additionally, the technique is shown to be easy to implement, to
require very low data storage, and to allow easy estimation of the entire
cycle-time cumulative distribution function.
PLSE-Based Generic Simulation Training Platform for
Typical Weapon Equipments
Ying Liu (Mechanical Engineering College)
Abstract:
With the development of simulation technologies,
virtual training for the military has become more and more important.
Combining system and software engineering theory, based on the PLSE (Product
Line Software Engineering) idea and method, we analyze and design simulation
training characteristics for typical field equipment. We set up the
domain-oriented system architecture and implement the domain framework. The
research involves: putting forward the conceptual simulation platform based on
PLSE, applying the domain engineering method, analyzing commonalities in the
training for typical equipment, and implementing a general architecture. Using
relevant technologies, we develop reusable core assets systematically and
strategically, and build the object-oriented development platform and the
platform-based developing models.
Understanding Accident and Emergency Department
Performance Using Simulation
Murat M. Gunal and Michael Pidd
(Lancaster University)
Abstract:
As part of a larger project examining the effect of
performance targets on UK hospitals, we present a simulation of an Accident
and Emergency (A&E) Department. Performance targets are an important part
of the National Health Service (NHS) performance assessment regime in the UK.
Pressures on A&Es force the medical staff to take actions meeting these
targets with limited resources. We used simulation modelling to help
understand the factors affecting this performance. We utilized real data from
patient admission system of an A&E and presented some data analysis. Our
particular focuses are the multitasking behaviour and experience level of
medical staff, both of which affect A&E performance. This performance
affects, in turn, the overall performance of the hospital of which it is part.
Simulation-Based Disaster Decision Support
System
Shengnan Wu, Larry Shuman, and Bopaya Bidanda (Industrial
Engineering University of Pittsburgh), Carey Balaban (Bioengineering
University of Pittsburgh) and Matthew Kelley and Ken Sochats (Information
Sciences University of Pittsburgh)
Abstract:
Intelligent control systems can assist decision makers
in addressing unanticipated events including disasters. We are developing
Dynamic Discrete Disaster Decision Simulation System
(D4S2) for planning improved responses to large-scale
disasters. D4S2 integrates agent-based and discrete
event simulation, a geographic information system and a knowledge-based system
into one platform to better assess how various decisions might impact the
evolving incident scene. This enables us to model human behavior during large
scale emergency incidents, incorporating methodologies from operations
research, information sciences and medical sciences into our model. We propose
that D4S2 can be used as a sequential decision making
tool. As the incident unfolds, decisions such as when and what type of
response to dispatch, and what actions should be taken at the scene change. By
dividing the incident into phases and simulating the potential result of one
phase while it is ongoing, more informed follow-up decision can be made.
Augmented Simultaneous Perturbation Stochastic
Approximation (ASPSA) Algorithm
Liya Wang (Penn State University)
Abstract:
In recent years, simulation optimization has attracted
a lot of attention because simulation can model the real systems in fidelity
and capture the dynamics of the systems. Simultaneous Perturbation Stochastic
Approximation (SPSA) is a simulation optimization algorithm that has attracted
considerable attention because of its simplicity and efficiency. SPSA performs
well for many problems but does not converge for some. This research proposes
Augmented Simultaneous Perturbation Stochastic Approximation (ASPSA) algorithm
in which SPSA is extended to include presearch, ordinal optimization,
non-uniform gain, and line search. Extensive tests show that ASPSA achieves
speedup and improves solution quality. ASPSA is also shown to converge. For
unconstrained problems ASPSA uses random presearch whereas for constrained
problems a line search is used to handle the additional complexity, thereby
extending the gradient based approach. Performance of ASPSA is tested for
supply chain inventory optimization problems including serial supply chain
without constraints and fork-join supply chain network with customer service
level constraints. Experiments show that ASPSA is comparable to Genetic
Algorithms in solution quality (worst case 6%) but is much more efficient
computationally (20x faster).
Overlapping Folded Variance Estimators for
Stationary Simulation Output
Melike Meterelliyoz, Christos
Alexopoulos, and David Goldsman (Georgia Institute of Technology)
Abstract:
We propose and analyze a new class of estimators for
the variance parameter of a steady-state simulation output process. The
estimators are computed by averaging "folded" versions of the standardized
time-series corresponding to overlapping batches of consecutive observations.
We establish the limiting distributions of the proposed estimators as the
sample size tends to infinity while the ratio of the sample size to the batch
size remains constant. Compared with their counterparts, the new estimators
have roughly the same bias but smaller variance. These estimators can be
computed with order-of-sample-size work with the efficient algorithms that we
formulate. To complement these, we provide Monte Carlo results for specific
examples. Finally, the asymptotic distributions of the proposed estimators are
found to be closely approximated by a rescaled chi-squared random variable
whose scaling factor and degrees of freedom are set to match the mean and
variance of the target asymptotic distribution.
Simulation and Optimization of Control Strategies
for the Semiautomatic Processing of Returns in Commercial
Logistics
Helena Tsai (Fraunhofer Institute for Material Flow and
Logistcs)
Abstract:
Constantly increasing importance of e-commerce,
shortened product life-cycles and more demanding customers cause a growing
number of goods returned. Companies have to adapt their returns-processes to
the recent developments in order to reduce their costs and to save customers
loyalty. The main subject of research is to study how different manual
sorting- and picking-strategies influence the performance of a semiautomatic
return-processing-system that is characterized by a constantly changing system
load and article spectrum. The aim of this work is to improve the performance
of the current system in dependence of selected control strategies and system
parameters by organizational measures. A high level of complexity, caused
mainly by manual operations and the extremely stochastic demand require the
application of simulation. This work is sponsored by an industrial partner who
operates such a returns-processing-system for a large retailer. The results of
the simulation are going to be implemented in the real system.
On-Line Instrumentation for Simulation-Based
Optimization
Anna Persson and Amos Ng (University of Skovde)
Abstract:
Traditionally, a simulation-based optimization (SO)
system is designed as a black-box in which the internal details of the
optimization process is hidden from the user and only the final optimization
solutions are presented. As the complexity of the SO systems and the
optimization problems to be solved increases, instrumentation – a technique
for monitoring and controlling the SO processes – is becoming more important.
This paper proposes a white-box approach by advocating the use of
instrumentation components in SO systems, based on a component-based
architecture. This paper argues that a number of advantages, including
efficiency enhancement, gaining insight from the optimization trajectories and
higher controllability of the SO processes, can be brought out by an on-line
instrumentation approach. This argument is supported by the illustration of an
instrumentation component developed for an SO system designed for solving
real-world multi-objective operation scheduling problems.
Scalability Assessment of Multi-Agent Simulation
Using BioWar and Spread of Influenza
Virginia Bedford, Kathleen
Carley, and Il-Chul Moon (Carnegie Mellon University) and Bruce Lee
(University of Pittsburgh)
Abstract:
High fidelity multi-agent simulation systems are needed
for training and planning in areas such as the spread of infectious disease.
However, as the fidelity of the models increases so do the required
computational time and storage requirements. Major efficiencies would be
achieved if we could run the model with fewer agents and extrapolate the
behavior to larger groups. Such models could be thought of as having scalable
results. We examine what types of results are likely to be scalable for
epidemiological models by using the BioWar simulation model, a citywide model
of epidemiological and chemical events. We examine the spread of influenza in
Norfolk, Virginia. We consider peak size and day of infection, shape of
infection curves, and effects of scale on subpopulation age-groups to discover
whether increasing granularity increases fidelity and whether there are
certain thresholds beyond which increasing the granularity does not yield
substantial gains in fidelity.
Discrete-Event Simulation - An Approach That
Challenges Traditional Decision Analytic Modelling for the Comparison of
Health Care Interventions?
Beate Jahn and Karl-Peter Pfeiffer
(Innsbruck Medical University, Department for Medical Statistics, Informatics
and Health Economics)
Abstract:
OBJECTIVES: Discrete-event simulation is rarely used
for comparative analyses of medical treatments. To illustrate the benefits,
treatments for cardiovascular disease (drug-eluting-stents /
bare-metal-stents) are evaluated. This methodological study demonstrates how
capacity constraints affect cost-effectiveness and additional parameters for
decision making. METHODS: Cost-effectiveness analysis for newly developed
treatments is usually done assuming unrestricted availability of capacities,
or the capacity constraints are incorporated addressing only fixed waiting
times. This is mainly because traditional modelling techniques do not provide
the necessary flexibility. A discrete-event simulation is used to estimate the
outcomes of stent treatments under the assumption of several capacity
restrictions. RESULTS: Capacity limitations change cost-effectiveness results.
Treatment strategies become dominated and should therefore not be applied.
Furthermore, cost-effectiveness results, utilization and budgetary impacts can
be evaluated within one simulation. CONCLUSIONS: Discrete-event simulation
provides a wide range of multiple perspective outcomes. Incorporated
capacities and potential limitations have a significant impact on model
outcomes and decision making.
Optimizing Importance Sampling Parameter for
Portfolios of Credit Risky Assets
Huiju Zhang and Michael Fu
(University of Maryland)
Abstract:
Accurate assessments of potential losses on a credit
portfolio play a key role in the financial management. Monte Carlo simulation
with importance sampling is widely applied to determine the loss distribution
for a credit portfolio. We cast the selection of importance sampling measure
change parameter as a minimization problem, and apply a gradient-based
stochastic algorithm and Cross-Entropy method to estimate the optimal measure.
Both algorithms converge efficiently to the optimum.
Modeling Tuberculosis In Areas Of High HIV
Prevalence Using Discrete Event Simulation
Georgina Rosalyn Hughes
and Christine Currie (University of Southampton) and Elizabeth Corbett (London
School of Hygiene and Tropical Medicine)
Abstract:
Tuberculosis (TB) and HIV are the leading causes of
death from infectious disease among adults worldwide and the number of TB
cases has risen significantly since the start of the HIV epidemic. There is a
need to devise new strategies for TB control in countries with high HIV
prevalence. The current policy of active case finding was developed in an era
of low HIV prevalence and the impact of the HIV epidemic on the relative
importance of household versus community transmission of TB has not been fully
assessed. We describe a discrete event simulation model of TB and HIV disease,
parameterized to describe the dual epidemics in Harare, Zimbabwe. The aim of
the research is to explore the likely impact of different TB control
interventions, focusing in particular on the role of close versus casual
contacts in the transmission of TB.
Stochastic Gradient Estimation Using a Single
Design Point
Jamie R. Wieland (Purdue University) and Bruce W.
Schmeiser (Purdue Universtiy)
Abstract:
Using concepts arising in control variates, we propose
estimating gradients using Monte Carlo data from a single design point. Our
goal is to create a statistically efficient estimator that is easy to
implement, with no analysis within the simulation oracle and no unknown
algorithm parameters. We compare a simple version of the proposed method to
finite differences and simultaneous perturbation. Results derived from an
assumed second-order linear model illustrate that the statistical performance
of the proposed method appears to be competitive with that of existing
methods.
