WSC 2008 Final Abstracts |
Monday 10:30:00 AM 12:00:00 PM
Design of Simulation Experiments
Chair: Gerald Mackulak (Arizona State University)
Comparing Designs for Computer Simulation
Experiments
Rachel Terese Johnson and Douglas C Montgomery (Arizona
State University), Bradley Jones (SAS Institute) and John W Fowler (Arizona
State University)
Abstract:
The use of simulation as a modeling and analysis tool
is wide spread. Simulation is an enabling tool for experi-menting virtually on
a validated computer environment. Often the underlying function for the
results of a computer simulation experiment has too much curvature to be
adequately modeled by a low order polynomial. In such cases finding an
appropriate experimental design is not easy. This research uses prediction
variance over the volume of the design region to evaluate computer simulation
experiments assuming the modeler is interested in fitting a second order
polynomial or a Gaussian Process model to the response data. Both
space-filling and optimal designs are considered.
Using Simulation Early in the Design of a Fuel
Injector Production Line
Mustafa H. Tongarlak, Bruce Ankenman, and
Barry L Nelson (Northwestern University) and Laurent Borne and Kyle Wolfe
(Delphi Corporation)
Abstract:
Delphi Corporation decided to use simulation from
concept development to installation of a new multimillion dollar fuel injector
production line. In this paper we describe how simulation was employed in the
concept development phase to assess whether production targets required for
financial viability were feasible and to identify the critical features of the
line on which to focus design-improvement efforts.
Design of Experiments: Overview
Jack
P.C. Kleijnen (Tilburg University)
Abstract:
Design Of Experiments (DOE) is needed for experiments
with real-life systems, and with either deterministic or random simulation
models. This contribution discusses the different types of DOE for these three
domains, but focusses on random simulation. DOE may have two goals:
sensitivity analysis and optimization. This contribution starts with classic
DOE including 2(k-p) and Central Composite Designs (CCDs). Next, it discusses
factor screening through Sequential Bifurcation. Then it discusses Kriging
including Latin Hypercube Sampling and sequential designs. It ends with
optimization through Generalized Response Surface Methodology and Kriging
combined with Mathematical Programming, including Taguchian robust
optimization
Monday 1:30:00 PM 3:00:00 PM
Simulation Optimization
Chair:
Victor Chan (Rensselaer Polytechnic Institute)
Large Deviations Perspective on Ordinal
Optimization of Heavy-Tailed Systems
Jose Blanchet (Columbia
University), Jingchen Liu (Harvard University) and Bert Zwart (Georgia Tech)
Abstract:
We consider the problem of selecting the best among
several heavy-tailed systems from a large deviations perspective. In contrast
to the light-tailed setting studied by Glynn and Juneja (2004), in the
heavy-tailed setting, the probability of false selection is characterized by a
rate function that does not require as detailed information about the
probability distributions of the system's performance. This motivates the
question of studying static policies that could potentially provide convenient
implementations in heavy-tailed settings. We concentrate on studying sharp
large deviations estimates for the probability of false detection which
suggest precise optimal allocation policies when the systems have comparable
heavy-tails. Additional optimality insights are given for systems with
non-comparable tails.
Mathematical Programming Representations for
State-Dependent Queues
Wai Kin Victor Chan (Rensselaer Polytechnic
Institute) and Lee Schruben (University of California, Berkeley)
Abstract:
Discrete-event dynamic systems with feedback, where the
behavior of the system depends on the system state, are difficult to model due
to the uncertainties and dependencies of system performance on the system
state. Service systems, in particular, tend to exhibit this behavior where
servers may work faster (or slower) when facing an increasingly long line of
impatient customers. A common example is a state-dependent queue where the
service rate depends on the queue size, which can change during service. In
this paper, we present a mathematical programming representation for the
sample path dynamics of a state-dependent queue, and illustrate its
application in sensitivity analysis.
Discrete Stochastic Optimization Using Linear
Interpolation
Honggang Wang and Bruce W Schmeiser (Purdue
University)
Abstract:
We consider discrete stochastic optimization problems
where the objective function can only be estimated by a simulation oracle; the
oracle is defined only at the discrete points. We propose a method using
continuous search with simplex interpolation to solve a wide class of
problems. A retrospective framework provides a sequence of deterministic
approximating problems that can be solved using continuous optimization
techniques that guarantee desirable convergence properties. Numerical
experiments show that our method finds the optimal solutions for discrete
stochastic optimization problems orders of magnitude faster than existing
random search algorithms.
Monday 3:30:00 PM 5:00:00 PM
Queueing & Reinforced Learning
Chair: Feng Yang (West Virginia University)
Max-Min Optimality of Service Rates in Queueing
Systems with Customer-Average Performance Criterion
Li Xia, Ming
Xie, Wenjun Yin, and Jin Dong (IBM China Research Laboratory)
Abstract:
In this paper, we consider the optimization of service
rates in queueing systems, especially in closed Jackson networks. The
optimization criterion is the customer-average performance, which is another
important performance metric compared with the traditional time-average
performance. Based on the methodology of perturbation analysis, we can derive
a performance difference equation when the service rates are changed. With
this difference equation, we find the optimal service rates have a Max-Min
property, i.e., the optimal service rates can be chosen from its maximal or
minimal value. This property can reduce the complexity of this type of
optimization problems. Moreover, we also prove the max-min optimality is valid
for both state-dependent service rates and load-dependent service rates in
queueing systems.
Evaluating the Transient Behavior of Queueing
Systems via Simulation and Transfer Function Modeling
Jingang Liu
and Feng Yang (West Virginia University)
Abstract:
Characterizing the transient behavior of queueing
systems is a difficult problem, which has been addressed by either simplified
analytical models or simulation. We seek to capture the transient performance
of systems from a new perspective: based on high-fidelity simulation
experiments, we estimate a number of transfer function models (the discrete
approximations of those ODEs provided by an analytical approach) which
characterizes the evolution of the system's dynamic behavior.
On Step Sizes, Stochastic Shortest Paths, and
Survival Probabilities in Reinforcement Learning
Abhijit Gosavi
(Missouri University of Science and Technology)
Abstract:
Reinforcement Learning (RL) is a simulation-based
technique useful in solving Markov decision processes if their transition
probabilities are not easily obtainable or if the problems have a very large
number of states. We present an empirical study of (i) the effect of
step-sizes (learning rules) in the convergence of RL algorithms, (ii)
stochastic shortest paths in solving average reward problems via RL, and (iii)
the notion of survival probabilities (downside risk) in RL. We also study the
impact of step sizes when function approximation is combined with RL. Our
experiments yield some interesting insights that will be useful in practice
when RL algorithms are implemented within simulators.
Tuesday 8:30:00 AM 10:00:00 AM
Warm-Up Analysis
Chair: Stewart
Robinson (The University of Warwick)
Automating Warm-Up Length
Estimation
Kathryn Hoad, Stewart Robinson, and Ruth Davies (Warwick
Business School)
Abstract:
There are two key issues in assuring the accuracy of
estimates of performance obtained from a simulation model. The first is the
removal of any initialisation bias, the second is ensuring that enough output
data is produced to obtain an accurate estimate of performance. This paper is
concerned with the first issue, and more specifically warm-up estimation. A
continuing research project is described that aims to produce an automated
procedure, for inclusion into commercial simulation software, for estimating
the length of warm-up and hence removing initialisation bias from simulation
output data.
Stationarity Tests and MSER-5: Exploring the
Intuition Behind Mean-Squared-Error-Reduction in Detecting and Correcting
Initialization Bias
William W Franklin and K. Preston White
(University of Virginia)
Abstract:
We explore the reasoning behind MSER-5, an efficient
and effective truncation heuristic for reducing initialization bias in
steady-state simulation. We also compare MSER-5 with the KPSS stationarity
test as one means of investigating the possibility that MSER’s effectiveness
is the result of its utility as a stationarity measure. Conversely, this
comparison also lets us explore whether or not a stationarity test from the
time-series literature can be used as an effective initialization bias-control
heuristic. Finally, we investigate the use of an alternative form of MSER-5
that uses a variance estimator that adjusts for serial correlation.
Using Slithers of Simulation in a New Approach
for Intelligent Initialization of Non-Terminating Systems
Philip G
Brabazon (Nottingham University Business School)
Abstract:
Reduction and even avoidance of the initial transient
is known to be possible using intelligent initialization but it is not an
approach that has been actively researched for some time. Using ideas from
complexity science and informed by the recently developed equation-free
technique a new method for identifying initializing conditions is developed.
Many short bursts of simulation, called slithers, are used to construct the
dynamic function of a system. Importantly, the function reveals the point
attractor of the dynamic system to which it will evolve and the conditions at
the attractor define the initializing conditions for future simulation runs.
The method is demonstrated by application to a queuing network.