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