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

processes [Kwiatkowska et al., 2008] and Randomised distributed algorithms [Kwiatkowska and
Norman, 2002]. We will use PRISM as the underly decision procedure for formal stochastic analysis.
We will develop a pattern-based method for a user to specify stochastic properties of a supply
chain. We also will extend the decision algorithm in PRISM so we can extract the proof from a
model-checking run and develop a game-theoretic approach to interpret the proof to an end user.
On the implementation side, Rossetti et al. [2006] used an objective-oriented framework to
implement a Java package for supply-chain simulation. Liu et al. [2004] introduced a Java-based
supply-chain simulation tool Easy-SC. In Easy-SC modeling environment, facilities were modeled
as instances of pre-defined six enterprise nodes, and routes were implemented as connection arcs.
Wang et al. [2008] discussed a general business simulation environment (GBSE) developed by IBM
China research lab. GBSE was a Java-based event-driven simulation tool built on top of the
Eclipse platform [the Eclipse Foundation, since 2004]. Our proposed open-source tool will also be
developed on Eclipse for better extensibility. In contrast to GBSE’s conventional implementation
of a simulator, in which the simulation logic is integrated with the tool, our tool will implement
a generative simulation engine, which will generate stand-alone simulators tailored for targeted
hardware architectures.
3.2 Preliminary Study
To demonstrate benefits and feasibility of our ideas, we have conducted a preliminary study on
components of the proposed research.
In [Tan and Xu, 2008] we proposed a model-checking-based formal stochastic analysis framework
for supply chains. The framework used an extension of Markov decision processes to model
elements of a supply chain. We encoded stochastic properties of a supply chain in the Probabilistic
Computational Tree Logic (PCTL). To the best of our knowledge, this was the first published
work on applying probabilistic model checking technique to supply chain analysis. In [Tan and
Xu, 2009a] we further studied the benefits of the formal stochastic analysis by using it to analyze
risks in supply chain consolidation. We conducted a quantitative analysis of the impact of four
consolidation strategies on risks in a 4-echelon supply chain. Figure 1 shows the configurations of
supply chain networks used in the experiments in [Tan and Xu, 2009a]. Figure 1.(a) shows two independent
networks. Figure 1.(b) shows the consolidated supply chain resulting from a merger and
acquisition with product pooling strategy. Figure 2 reports a result of formal stochastic analysis. It
shows the probability of on-time deliveries under different supply-chain consolidation strategies. In
[Tan and Xu, 2009b] we tested an implementation of agent-based stochastic modeling approach for
supply chains. The implementation included an object-oriented type system for improving model
reusability. Additional results from our preliminary study were also presented in [Xu and Tan,
2008, Tan and Xu, 2009c].
4 Relations to the principal investigators’ long-term goals
The long term goal of this research is to advance modeling and automated analysis technologies
for analyzing stochastic and temporal behaviors of large-scale supply chains. The proposed project
represents a significant step towards this long-term goal. Our preliminary study has shown the
feasibility and benefits of every technological components in this proposed research, including agentbased
stochastic modeling [Tan and Xu, 2008], generative simulation technology [Tan and Xu,
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