SIMULATION: Transactions of The Society for Modeling and Simulation International

Jin, Jing; Shahbazi, Sahar; Lloyd, John; Fels, Sidney; de Ribaupierre, Sandrine; Eagleson, Roy

doi: 10.1177/0037549713516691pmid: N/A

This paper describes a hybrid model that includes both a standard finite element model and also volume-preserving structural modeling for a clinical application involving skull development in infants, with particular application to craniostosis modeling. To accommodate the growing brain, the skull needs to grow quickly in the first few months of life, and most of the growth of the skull at that time occurs at the sutures. Craniosynostosis, which is a developmental abnormality, occurs when one or more sutures are fused early in life (even in utero) while the skull is growing, resulting in an abnormal skull shape. To study normal brain–skull growth and to develop a model of craniosynostosis, we have developed a hybrid computational model to simulate the relationship between the growing deformable brain and the rigid skull. Our model is composed of the nine segmented skull plates as rigid surfaces, deformable sutures, and a volumetrically controllable deformable brain. The Cranial Index (ratio of biparietal width to fronto-occipital length) is measured during the simulation, showing a characteristic peak during development. Measures of linear growth along each dimension show characteristic increases over time. The hybrid simulation framework shows promise to support further investigations into abnormal skull development. By varying the properties of the sutures in our model, we can now simulate different craniosynostosis models, such as scaphocephaly and trigonocephaly. In this paper, we show results on the evolution of the Cranial Index as calculated using standard landmarks and compare to the normal index, and thereby evaluate our model by comparing it with patient data.

Grassmann, Winfried K

doi: 10.1177/0037549713508334pmid: N/A

In this paper, we discuss the factors affecting the initialization bias in discrete event simulation. Specifically, we assume that the time average is used to find the equilibrium expectation of a certain variable R, say the number in a queueing network, and we would like to minimize the mean squared error (MSE) between the time average of R and its equilibrium expectation. To do this, a warm-up period is often used during which no data is collected, and we want to find the length of this period such that the MSE is minimal. We show that if starting in what Tocher calls a “typical condition”, warm-ups tend to be redundant. This result is strengthened by theoretical arguments and numerical experiments. If starting in a typical state is inconvenient, warm-up periods should be used, and methods to find optimal warm-up periods are discussed. The numerical methods used for our experiments do not rely on Monte Carlo simulation. Instead, we determine the MSE of the time average by the randomization method and other deterministic methods.

Kamkarian, Pejman; Hexmoor, Henry

doi: 10.1177/0037549713509416pmid: N/A

The Imperialist Competition Algorithm has recently shown superior performance in optimizing goals. This algorithm has inspired us to apply it to exit doors. The Imperialist Competition Algorithm is applied to an environment to find the best possible locations for exit doors and also to estimate the minimum required width for each door in order to be able to rapidly evacuate a crowd out of danger in emergency situations. The results of our system are applied to a few prototypical scenarios that have demonstrated that the location of each exit door in an indoor space can play a significant role in the evacuation of a crowd out of an emergency situation. Our results thus far are promising and future work will account for more complex floor layouts.

Miller, Matt L; Ringelman, Kevin M; Schank, Jeffrey C; Eadie, John M

doi: 10.1177/0037549713511864pmid: N/A

The management of North American waterfowl is widely recognized as a premier example of a successful conservation program. Conservation managers on the wintering grounds typically use simple estimates of food availability and population-wide cumulative energy demand to determine how many birds can be supported on a given landscape. When attempting to plan for future needs due to land reallocation, climate change, and other large-scale environmental changes, simple bioenergetic models may not capture important impacts on individual behavior, such as changes in metabolic costs due to increased travel-time and reduced food accessibility leading to non-linear declines in forager success. We describe the development of an agent-based model of foraging waterfowl that uses explicit individual behavior to generate more detailed and potentially more accurate insights into the impact of environmental changes on forager success and survival. While there is growing recognition of the potential utility of agent-based models in conservation planning, there has yet to be an attempt to formulate, validate, and communicate such a model for use as a decision support tool to guide habitat management conservation for wetlands in North America. Our model seeks to provide the foundational framework for such an effort. We predict that this model will be a useful tool for stakeholders making conservation management decisions.

Hertz, Philipp; Cavalieri, Sergio; Finke, Gandolf R; Duchi, Aldo; Schönsleben, Paul

doi: 10.1177/0037549713512685pmid: N/A

Technical field services for industrial machinery and equipment have become increasingly important for original equipment manufacturers. To deliver services to their customers, companies have to build up new core competencies and infrastructure, a challenge due to the high complexity and dynamics of this business. To assist companies in the strategic design of their network and the planning of resources for delivering industrial field services, we present a model-driven decision support system that uses discrete event simulation to support decision makers in various aspects of strategic design and tactical planning. The benefits of the decision support system include the creation of a generic framework that makes it possible to create simulation models of different field service networks for multiple purposes. Specifically, the system can be used to support various tactical planning and strategic design decisions while keeping investments low in terms of time consumption and money spending. In addition, the paper closes an identified gap involving a lack of decision support for the management of field service networks. An application of the decision support system in an exemplary case is used to illustrate potential applications and benefits.

Schenk, Tilman A

doi: 10.1177/0037549713514127pmid: N/A

When reviewing the present state of the art in agent-based models of social processes, one may encounter three basic types of models: theory-based models start with a set of assumptions about human behavior and test those assumptions in various scenarios; data-driven models are based on mass data (e.g. census, large surveys) and usually include a large number of agents; a third group of models combines different data sources and may also include qualitative information, that is, text descriptions of human behavior. Pushing this notion further, the motivation of this paper is to demonstrate how an agent-based model of a political process can be designed only using the stakeholders’ own descriptions and observation results. The simulation reproduces the discussions among the stakeholders and their subsequent decisions and is able to react to changes in their general settings. The simulation outcomes are also produced in text format so that they are easily understandable to stakeholders and other users. The simulation can be used to explore different sets of rules for the decision processes and their results.

Blagojević, Miloš; Geilen, Marc; Basten, Twan; Nabi, Majid; Hendriks, Teun

doi: 10.1177/0037549713515028pmid: N/A

Gossip-based Wireless Sensor Networks (GWSNs) are complex systems of inherently random nature. Planning and designing GWSNs requires a fast and adequately accurate mechanism to estimate system performance. As a first contribution, we propose a performance analysis technique that simulates the gossip-based propagation of each single piece of data in isolation. This technique applies to GWSNs in which the dissemination of data from a specific sensor does not depend on dissemination of data generated by other sensors. We model the dissemination of a piece of data with a Stochastic-Variable Graph Model (SVGM). A SVGM is a weighted-graph abstraction in which the edges represent stochastic variables that model propagation delays between neighboring nodes. Latency and reliability performance properties are obtained efficiently through a stochastic shortest-path analysis on the SVGM using Monte Carlo (MC) simulation. The method is accurate and fast, applicable for both partial and complete system analysis. It outperforms traditional discrete-event simulation. As a second contribution, we propose a centrality-based stratification method that combines structural network analysis and MC partial simulation, to further increase efficiency of the system-level analysis while maintaining adequate accuracy. We analyzed the proposed performance evaluation techniques through an extensive set of experiments, using a real deployment and simulations at different levels of abstraction.