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

party to develop analysis plugins for Simrisk. We will also develop an object-oriented type system
for supply chains, by which a practitioner defines new supply-chain elements suitable for his/her
application.
Our interdisciplinary team possesses expertise and skills essential for the success of the proposed
research. Dr. Li Tan is on computer science faculty in Washington State University. He conducted
research on model checking, model-based design and simulation, and software analysis. Dr. Shenghan
Xu is on the business faculty of the University of Idaho. Her research is on supply-chain
management. We already collaborated in the preliminary study of this research on the components
of the proposed project and published the results. For example, in [Tan and Xu, 2009b] we developed
a prototype of an agent-based supply chain modeling and simulation tool; we introduced a
probabilistic-model-checking formal stochastic analysis technique in [Tan and Xu, 2008] and used
it to analyze risks in supply chain consolidation in [Tan and Xu, 2009a]. Please see Section 3.2 for
more details. Our team members also developed a range of open-source tools in model checking
[Cleaveland et al., 2000], model-based design [Tan, 2006], and supply chain modeling and analysis
[Tan and Xu, 2009b] etc.
2 Expected Significance
The importance of stochastic supply-chain analysis is acknowledged in a wide range of applications,
including risk supply-chain risk analysis [Chen and Zhang, 2008], contracting [van Delft and Vial,
2004], and performance evaluation [Wei et al., 2007]. Existing stochastic analysis technologies and
tools do not possess efficiency, accuracy, scalability, and usability required by analyzing contemporary
global supply chains [Finch, 2004, Wu et al., 2006]. To reduce cost and maintain profit margin,
nowadays many companies engage themselves in global supply chain expansion involving suppliers,
distributors, retailers, and logistics providers across multiple continents [Ferrer and Karlberg,
2006]. For example, the Sears Holding company operates more than 3,800 full-line and specialty
retail stores including Kmart and Sears stores in the United States and Canada [Sears Holding
Company, 2008]. The wholesale chain Costco operates its 544 warehouse stores in North America,
South America, Asia, and Europe. It sources merchandise from all over the world [Costco Wholesale
Corporation, 2007]. The existing stochastic analysis technologies cannot meet the demand of
analyzing large-scale supply chains in terms of scalability, efficiency, accuracy, and usability.
The purpose of this project is to develop an open-source tool and its underlying technologies
that are efficient and scalable for analyzing and optimizing real-world large-scale supply chains
under uncertainty. Specifically we expect that this research will advance the state-of-the-art in the
following technologies: agent-based supply chain modeling, generative parallel simulation technology,
and formal analysis and optimization technique. We will leverage recent advances in software
engineering, computer architecture, and formal methods, and apply them to stochastic supply-chain
analysis. By doing so, this project also promotes synergy between computer science and operations
management. Technology advance achieved by this project will be delivered in an open-source tool.
The tool will empower practitioners to analyze risks and uncertainty arising from interactions of
different elements in supply chains on much larger scale and at finer granularity. Analysis result can
be used to help companies improve the design and management of their supply chains. The project
will also enable sophisticated analysis on complex stochastic properties and “what-if” scenarios, it
will help companies balance risk management with other operational factors, and streamline their
supply-chain operations.
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