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

Project Summary
This grant provides funding for developing novel methodologies and an open-source tool for
modeling, simulating, and formally analyzing and optimizing large-scale supply chains under uncertainty.
With increasing integration of the world’s economy, the size and complexity of global
supply chains are rising rapidly, so is the reliance of our economy on these supply chains. Understanding
and optimizing stochastic behaviors of these large-scale global supply chains is crucial for
predicating and managing their uncertainty and risks. To attack computational challenges arising
from the scale and complexity of these global supply chains, this interdisciplinary research will seek
synergetic breakthroughs in modeling, analyzing, optimization, and implementation. Specific topics
will include: 1) developing an agent-based stochastic modeling framework, which will model the
elements of a supply chain as autonomous Markov decision processes. The agent-based framework
will facilitate the study of complex stochastic behaviors arising from interactions of these elements;
2) developing a parallel simulation technology using generative software engineering methods; 3)
developing an efficient and rigorous automated formal stochastic analysis and optimization technique,
which is based on probabilistic model checking techniques developed in computer science; 4)
developing an open-source tool to disseminate the technologies obtained in this project. Preliminary
study and tools development have shown the benefits of this interdisciplinary research.
Intellectual Merits If successful, this project will significantly improve existing supply-chain
stochastic analysis and optimization technologies and tools in terms of efficiency, scalability, accuracy,
and usability. Specifically, the agent-based stochastic modeling framework will extend
agent-based modeling with the ability of modeling stochastic behaviors of a supply chain. To improve
efficiency and scalability, the generative simulation technology will harness computational
power brought by recent advances on multi-core hardware and Peta-level computing platforms.
The formal analysis and optimization technique will preserve the accuracy of deductive proof but
will greatly improve its efficiency. The project will continue the interdisciplinary study started in
[Tan and Xu, 2008, 2009a], and extends probabilistic model checking technology to supply-chain
risk analysis. The project will produce an open-source tool as technology delivery platform, which
will empower practitioners to conduct intensive analysis of risks and other stochastic factors for a
global supply chain with the scale and the complexity far beyond the current technologies allow.
Broader Impact Technologies developed in the project will be published in leading journals and
major conferences in operations management and computer science. The majority of this grant will
be used to support a junior women faculty member and a Ph.D. student. Part of this research will
be built into the current and future courses on supply-chain management, mathematical modeling,
software engineering, and parallel computing in Washington State University and the University of
Idaho. Situated in two geographically adjacent rural areas, both universities have a long tradition
of supporting women students and students from underrepresented groups in their business and
engineering programs. Last but not least, the open-source tool will be freely available. Lecturers
may use it to help students develop intuition behind supply-chain risk management and try “whatif”
scenarios for assessing different risk management strategies. Practitioners can use it, for instance,
to analyze risks arising from interactions between different elements of a global supply chain and
identify deficiency in risk management plans. The analysis results will help companies improve the
security of their supply chains, and in a sense, strengthen the security of our economy as a whole.
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