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

Server 1/Core 3
Server 2/Core 3
s a
s b
u a1 /d a1
w11 a w11
b u w12 a w11
b b1 /d b1 u a1 /d a1 u b1 /d b1
u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2
w a 21 w b 21
w a 22 w b 22
w a 23 w b 23
w a 24 w b 24
r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14 r 15 r 16
Server 1/Core 1 Server 1/Core 2 Server 2/Core 1 Server 2/Core 2
Figure 6: A distribution of threads for simulating the model in Figure 1.(b) on the cluster in Figure
5.
5.3 Specific aim 3: develop efficient formal stochastic analysis technique for
supply chains
Model checking [Clarke et al., 1986] is an automated verification technique in which the specification
of a system is formally encoded in a temporal logic. Model checking provides a “push-button”
approach to algorithmically verify a system on a temporal property and produce a mathematically
sound proof for its finding. Temporal logics used in model checking allow precise formulation of
subtle temporal properties. Model checking technology has been actively investigated and heavily
invested in industry and academia. Over years new techniques such as Binary Decision Graphbased
model checking [Burch et al., 1992] and SAT-based bounded model checking [Biere et al.,
1999] have drastically improved the scalability of model checking technology. Today model checking
has been successfully used for verifying industrial-size processor designs [Gerth, 2001] and software
applications [Ball et al., 2004]. Recently probabilistic model checking gains momentum by the
introduction of new symbolic techniques [Wang and Kwiatkowska, 2005]. It has been successfully
used to analyze a wide range of applications such as biological processes [Kwiatkowska et al., 2008]
and Randomised distributed algorithms [Kwiatkowska and Norman, 2002].
In this project we will develop a formal stochastic analysis technique derived from probabilistic
model checking. Particularly we will focus on the following issues in applying probabilistic model
checking to stochastic analysis of supply chains:
1. Pattern-based stochastic problem formulation. Problem formulation is the first step towards
formal stochastic analysis of supply chains. Probabilistic model checking formulates stochastic
properties in a probabilistic temporal logics. Using temporal logics requires some knowledge in
formal methods and may pose a hurdle for the adoption of new model-checking-based analysis
technique. To ease such difficulty, we propose a two-step process for problem formulation:
we will provide patterns for practitioners in supply-chain management to define stochastic
properties of interests in a language closer to their background, and we will develop an
algorithm that can translate a pattern-based representation of stochastic property to one in
a temporal logic.
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