WSC 2007 Final Abstracts

Panel: Distributed Simulation in Industry - a Real-world Necessity or Ivory Tower Fancy?
Peter Lendermann (D-SIMLAB Technologies), Leon F. McGinnis (Georgia Institute of Technology), Charles R. McLean (National Institute of Standards and Technology), Simon Taylor (Brunel University), Matthias Heinicke (Unigraphics Solutions GmbH) and Steffen Strassburger (Technical University of Ilmenau)

Abstract:
Distributed simulation has a long history at the Winter Simulation Conference. Although successful in the military domain it appears, however, that the idea of applying distributed simulation in other fields for modeling and analysis of large-scale, heterogeneous systems such as communication networks or supply chains has still not reached the stage of commercial use until today. This panel attempts to identify reasons for this phenomenon by debating whether distributed simulation is actually a real-world necessity or should rather be considered ivory tower fancy.

Representation and Simulation of Stochastic Petri Net Models Using xPNML
Hyunsoo Lee, Bikram Sharda, and Amarnath Banerjee (Texas A&M University)

Abstract:
The paper presents an extensible Petri Net Markup Language (xPNML), which is an extended version of PNML. The xPNML format overcomes limitations associated with PNML structure for simulation and analysis of stochastic Petri nets. In addition, the proposed format supports modular concepts for creating specific models (for example, a module for machine operation including working, failed and repair states). These modules can be used as templates and exchanged among different net models. The paper provides the ontology of xPNML model and overview of its JAVA based implementation for simulation and analysis of stochastic Petri nets.

Simulation-based, Ontology Driven Resource Plan Development
Frank Boydstun (Tinker Air Force Base) and Mike Graul, Perakath Benjamin, and Arthur Keen (Knowledge Based Systems, Inc.)

Abstract:
This paper describes the use of a planning ontology of the domain of Aircraft Maintenance, Repair and Overhaul [MRO] at a USAF depot to produce a discrete event simulation model of the aircraft ramp. The ramp is a very flexible, critical, shared resource for aircraft production. The “found work” variability inherent in the MRO process forces the ramp allocations and routing to be modified frequently. Unplanned aircraft moves are expensive and propagate queuing congestion and more moves. Therefore, it is necessary to develop a Rapid Ramp Reconfiguration Plan (RRRP). Ontology models allow the language of the planner to be harmonized with the language of the simulation analyst to speed model development. Simulation based planning is useful to mitigate the impact of “discovered work” by enabling the evaluation of and best selection from thousands of potential ramp resource allocation scenarios.

Using Ontologies for Simulation Integration
Perakath Benjamin, Kumar Akella, and Ajay Verma (KBSI)

Abstract:
This paper describes the motivations, methods, and solution concepts for the use of ontologies for simulation model integration. Ontological analysis has been shown to be an effective initial step in the construction of intelligent systems. However, the modeling and simulation community has not taken advantage of the benefits of ontology management technology. Moreover, the popularity of semantic technologies and the semantic web has provided several beneficial opportunities for the modeling and simulation communities of interest. The paper outlines the technical challenges in simulation integration and describes an ontology-based method that addresses these challenges. An example military combat simulation application scenario is used to illustrate the practical benefits of the simulation model integration approach.

Using Meta-level Ontology Relations to Measure Conceptual Alignment and Interoperability of Simulation Models
Levent Yilmaz (Auburn University)

Abstract:
Engineering large and complex simulation systems is becoming more reliant on the reuse of existing simulation models. While existing technical standards facilitate syn-tactic and technical interoperability among disparate simulation models, there is still lack of formal methods that enable sound reasoning about the conceptual congruity of models that are selected for composition. This paper suggests a graph-theoretic approach to measure the extent of conceptual congruity of models within a new context. The premise of the approach is based on having contextualized models that provide introspective access to their metamodels. A metamodel associated with a reusable model entails a conceptualization of the domain in which it is originally designed to be situated in. The metamodels are used to instantiate a metagraph and graph distance metrics are used to measure the alignment of metamodels in the context of the new application domain.

Conceptual Modeling of Information Exchange Requirements Based on Ontological Means
Andreas Tolk (Old Dominion University) and Charles D. Turnitsa (Virginia Modeling Analysis & Simulation Center)

Abstract:
Unambiguous definition of the information exchanged between distributed systems is a necessary requirement for simulation system interoperability. The ontological spectrum categorizes ontological means and shows the various degrees of support from recovery and discovery to reasoning. These means are highly applicable in support of data engineering to define information exchange requirements and therefore can increase interoperability. If the systems interfaces and resulting information exchange requirements are captured using the appropriate metadata, these ontological means can be furthermore applied to unambiguously identify exchangeable information. This allows systems to exchange information based on self-organizing principles using what they can exchange and not on mandated specifications of what they should exchange.

From Domain Ontologies to Modeling Ontologies to Executable Simulation Models
Gregory A. Silver, Osama M. Al-Haj Hassan, and John A. Miller (University of Georgia)

Abstract:
Ontologies allow researchers, domain experts, and software agents to share a common understanding of the concepts and relationships of a domain. The past few years have seen the publication of ontologies for a large number of domains. The modeling and simulation community is beginning to see potential for using these ontologies in the modeling process. This paper presents a method for using the knowledge encoded in ontologies to facilitate the development of simulation models. It suggests a technique that establishes relationships between domain ontologies and a modeling ontology and then uses the relationships to instantiate a simulation model as ontology instances. Techniques for translating these instances into XML based markup languages and into executable models for various software packages are also presented.