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

1 Overview and objectives
Project Description
We propose to develop Simrisk, an integrated open-source tool for modeling, simulating, analyzing,
and optimizing stochastic behaviors of large-scale supply chains under uncertainty. With
increasing integration of the world’s economy, the size and the complexity of global supply chains
are also rising rapidly, and so is the reliance of our economy on these supply chains. Understanding
and optimizing stochastic behaviors of these large-scale global supply chains are essential for a
variety of topics in supply-chain management, including risk management[Chen and Zhang, 2008],
contracting [van Delft and Vial, 2004], and performance evaluation [Wei et al., 2007]. The existing
stochastic analysis methods for supply chains (cf. [Wu et al., 2006, Finch, 2004]) include stochastic
simulation (cf. [Smith et al., 2005]), deductive proof (cf. [Agrell et al., 2004]), and stochastic programming
(cf. [Santoso et al., 2005]). With the first method, stochastic analysis is carried out by
simulating a stochastic model under different sceneries and then using statistic methods to analyze
a large set of simulation results. A drawback of this method is that even with a large number
of simulation runs, analysis result is generally inconclusive because of statistic errors. To achieve
desired confidence, stochastic simulation often needs to be run for many different sceneries and
hence slows down the analysis process. With the second method, a typical workflow is to build an
(abstract) stochastic model of a supply chain and then to analyze the model by manually proving
its stochastic properties. This process often requires sophisticated mathematical skills and even
with such skills, one has to simplify a supply-chain model to make it for deductive proof. Clearly
it is not scalable to handle the size and complexity of today’s global supply chains, which may
consists of thousands of nodes[Wu et al., 2006, Finch, 2004]; with the third method, the problem of
optimizing the design of a supply-chain design is modeled as a mathematical programming problem.
Although stochastic programming method can be automated, its scalability and efficiency do
not meet the demand of analyzing large-scale supply chains due to the restriction of underlying
optimization solvers [Santoso et al., 2005]. With the size of today’s global supply chains and
recent alarming cases on risks in global supply chains, there are acute demands [Wu et al., 2006,
Finch, 2004] for stochastic analysis risk analysis approach that can be scalable, fast, accurate, and
easy-to-use. Such demands can not be met by existing technologies. In addition, recent advances
on multicore processors and Peta-level high performance computing platform provides additional
parallel computing powers on computers ranging from PCs to super computers. A research question
is how to harness these parallel computing powers to improve the efficiency and scalability of
existing analysis and optimization techniques for supply chains under uncertainty.
The goal of project is to greatly improve scalability, efficiency, accuracy, and usability of stochastic
supply-chain analysis and optimization technology. The technology and the tool developed
in this project can be used in a range of applications concerning stochastic analysis of large-scale
supply chains, for instance, supply-chain risk analysis and performance evaluation. To achieve
the goal of this research, we will deploy an interdisciplinary approach to develop and integrate the
following methodologies and techniques:
1. An agent-based stochastic modeling framework, which models elements of a supply chain as
autonomous Markov decision processes and formally defines operational semantics for these
elements and their interactions. Agent-based modeling (cf. [Axelrod, 1997]) was developed
to study complexity system behaviors arising from interactions of autonomous agents. One
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