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SEED Center for Data Farming (Simulation Experiments & Efficient Designs)

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As simple as possible, but no simpler: A gentle introduction to simulation modeling
(2006) Sanchez, Paul J.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR)
We start with basic terminology and concepts of modeling, and decompose the art of modeling as a process. This overview of the process helps clarify when we should or should not use simulation models. We discuss some common missteps made by many inexperienced modelers, and propose a concrete approach for avoiding those mistakes. After a quick review of event graphs, which are a very straightforward notation for discrete event systems, we illustrate how an event graph can be translated quite directly to a computer program with the aid of a surprisingly simple library. The resulting programs are easy to implement and computationally are extremely efficient. The first half of the paper focuses principles of modeling, should be of general interest. The second half will be of interest to students, teachers, and readers who wish to know how simulations models work and how to implement them from the ground up.
Impact of logistics on readiness and life cycle cost: A design of experiments approach
(2010) Kang, Keebom; McDonald, Mary L.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR); Graduate School of Business & Public Policy (GSBPP)
In this paper we develop two models that can be used to identify critical logistics factors that impact military readiness and the life cycle cost. The first one, a discrete-event simulation model, estimates the operational availability of a weapon system given input parameters under a certain scenario. The second one, a spreadsheet model, computes the life cycle cost using the same input parameters for the simulation model. Our approach is intended to serve as a basis for discussion between program offices concerned with cost and operational commands with operational availability.
Work smarter, not harder: Guidelines for designing simulation experiments
(2006) Sanchez, Susan M.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR); Graduate School of Business & Public Policy (GSBPP)
We present the basic concepts of experimental design, the types of goals it can address, and why it is such as important and useful tool for simulation. A well-designed experiment allows the analyst to examine many more factors than would otherwise be possible, while providing insights that cannot be gleaned from trial-and-error approaches or by sampling factors one at a time. We focus on experiments that can cut down the sampling requirements of some classic designs by orders of magnitude, yet make it possible and practical to develop a better understanding of a complex simulation model. Designs we have found particularly useful for simulation experiments are illustrated using simple simulation models, and we provide links to other resources for those wishing to learn more. Ideally, this tutorial will leave you excited about experimental designs - and prepared to use them - in your upcoming simulation studies.
Improved efficient, nearly orthogonal, nearly balanced mixed designs.
(2011) Vieira, Helcio Jr.; Kienitz, Karl Heinz; Sanchez, Susan M.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR)
Designed experiments are powerful ways to gain insights into the behavior of complex simulation models. In recent years, many new designs have been created to address the large number of factors and complex response surfaces that often arise in simulation studies, but handling discrete-valued or qualitative factors remains problematic. We proposed a framework for generating, with a (given) limited number of design pointsn, a design which is nearly orthogonal and also nearly balanced for any mix of factor types (categorical, numerical discrete, and numerical continuous) and/or mix of factor levels.
Very large fractional factorials and central composite designs
(2005) Sanchez, S.M.; Sanchez, P.J.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR)
Become an ISSE Expert
(Monterey, California; Naval Postgraduate School, 2015) SEED Center for Data Farming (Simulation Experiments & Efficient Designs); SEED Center
Assessing obstacle location accuracy in the REMUS unmanned underwater vehicle
(2004) Allen, Timothy E.; Sanchez, Susan M.; Buss, Arnold H.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR)
Navy personnel use the REMUS unmanned underwater vehicle to search for submerged objects. Navigation inaccuracies lead to errors in predicting the location of objects and thus increase post-mission search times for explosive ordnance disposal teams. This paper explores components of navigation inaccuracy using discrete event simulation to model the vehicle s navigation system and operational performance. The simulation generates data used, in turn, to build statistical models of the probability of detection, the mean location offset given that detection occurs, and the location error distribution. Together, these three models enable operators to explore the impact of various inputs prior to programming the vehicle, thus allowing them to choose combinations of vehicle parameters that reduce the offset error between the reported and actual locations.
Defense and homeland security applications of multi-agent simulations
(IEEE, 2007) Lucas, Thomas W.; Martinez, Felix; Roginski, Jonathan W.; Sickinger, Lisa R.; Sanchez, Susan M.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR)
Department of Defense and Homeland Security analysts are increasingly using multi-agent simulation (MAS) to examine national security issues. This paper summarizes three MAS national security studies conducted at the Naval Post- graduate School. The first example explores equipment and employment options for protecting critical infrastructure. The second case considers non-lethal weapons within the spectrum of force-protection options in a martitime environment. The final application investigates emergency (police, fire, and medical) responses to an urban terrorist attack. There are many potentially influential factors and many sources of uncertainty associated with each of these simulated scenarios. Thus, efficient experimental designs and computing clusters are used to enable us to explore many thousands of computational experiments, while simultaneously varying many factors. The results illustrate how MAS experiments can provide valuable insights into defense and homeland security operation.
A design of experiments approach to readiness risk analysis
(2006) Sanchez, Susan M.; Kang, Keebom; Doerr, Kenneth; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR); Graduate School of Business & Public Policy (GSBPP)
We develop a simulation model to aid in identifying and evaluating promising alternatives to achieve improvements in weapon system-level availability when services for system components are outsourced. Two outcomes are valued: improvements in average operational availability for the weapon system, and reductions in the probability that operational availability of the weapon system falls below a given planning threshold (readiness risk). In practice, these outcomes must be obtained through performance-based agreements with logistics providers. The size of the state space, and the non-linear and stochastic nature of the outcomes, precludes the use of optimization approaches. Instead, we use designed experiments to evaluate simulation scenarios in an intelligent way. This is an efficient approach that enables us to assess average readiness and readiness risk outcomes of the alternatives, as well as to identify the components and logistics factors with the greatest impact on operational availability.
A user's guide to the brave new world of designing simulation experiments
(2005) Kleijnen, Jack P.C.; Cioppa, Thomas M.; Sanchez, Susan M.; Lucas, Thomas W.; SEED Center for Data Farming (Simulation Experiments & Efficient Designs); Operations Research (OR)
Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. In this paper, we discuss a toolkit of designs for simulationists with limited DOE expertise who want to select a design and an appropriate analysis for their computational experiments. Furthermore, we provide a research agenda listing problems in the design of simulation experiments—as opposed to real world experiments—that require more investigation. We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system; (2) finding robust decisions or policies; and (3) comparing the merits of various decisions or policies. Our discussion emphasizes aspects that are typical for simulation, such as sequential data collection. Because the same problem type may be addressed through different design types, we discuss quality attributes of designs. Furthermore, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for example, more complicated metamodels require more simulation runs. For the validation of the metamodel estimated from a specific design, we present several procedures.