SimRisk: An Integrated Open-Source Tool for Agent-Based ...

enefit of agent-based modeling is that it scales well for systems with large numbers of autonomous
elements (agents). Recently it draws much research interest in simulating complex
social systems[Bonabeau, 2002]. Analyzing complex stochastic supply chains falls into the
type of problems that an agent-based approach is prescribed for: although the stochastic behavior
of each element is relatively easy to understand, uncertainty arising from interactions
among these elements is not. Although there are some prior works on agent-based approach
for supply chains with deterministic behaviors [Swaminathan et al., 1998], little research has
been done on addressing stochastic aspect of supply chains using agent-based approach. Since
understanding stochastic behaviors of supply chains is crucial for supply-chain risk analysis,
an objective of this project is to fill this gap by studying agent-based modeling for supply
chains under uncertainty.
2. Generative parallel simulation technology, which simulates a supply-chain model by generating
executable simulation code. Recent advance in multi-core hardware and the emerge
of peta-level parallel computing platforms provide extra parallel computing powers for computers
ranging from desktops to super computers. To take full advantage of these parallel
architectures, our generative simulation technology will generate executable simulation code
optimized for these parallel architectures.
3. Formal stochastic analysis and optimization technique, which is derived from probabilistic
model checking technique developed in computer science. In contrast to stochastic simulation,
the formal stochastic analysis technique constructs a rigorous proof in a fully automated
manner. It provides mathematically sound stochastic analysis for supply-chain applications
more concerning the accuracy of analysis result. Developed for verifying stochastic system
designs, probabilistic model checking has made significant progress in past several years with
the development of efficient symbolic algorithms and open-source tools ([Hinton et al., 2006]).
To the best of our knowledge, in [Tan and Xu, 2008] we are the first to introduce probabilistic
model checking for supply chain risk analysis. In this project we will conduct an extensive
interdisciplinary study on applying model-checking-based technology to supply-chain analysis.
We will address issues including pattern-based problem formulation, proof extraction, and
result interpretation. In [Xu and Tan, 2009] we proposed a model-checking-based approach
for scheduling optimization. In this project we will extend this work to optimize operations
of supply chains under uncertainty.
Aforementioned theoretical study lays the foundation for Simrisk, the open-source tool we will
develop as a platform to delivery new technologies technology delivery platform. Specifically, we
will develop an agent-based visual modeling language as Simrisk’s front end. Simrisk will include
a code generator that can product simulation code in C/C++ targeted for specific parallel platforms.
Simrisk will also have a formal analysis and optimization module that allows a user to
formally specify stochastic properties of interest and provides a push-button approach to analyze
and optimize stochastic supply chains with respect to these properties. The design of Simrisk
will emphasize on extensibility. Simrisk is not only an implementation of modeling and analysis
technologies developed in this project, but also an open platform which users may extend with
new functionalities. The tool will be built on Eclipse, a leading open-source software development
environment [the Eclipse Foundation, since 2004]. Eclipse is known for its extensibility: users can
extend its functionalities with customized plugins. We will define an interface which allows third
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