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

(b) Support component-based design. An important issue in model-based system design is
how to break a design model to more manageable pieces. This is especially important for
designing and modeling large-scale supply chains. Our agent modeling will address this
issue by supporting behaviorial composition and hierarchy. We will base our technique
on a proven component-based design technique, which is used in StateChart [Harel,
1987] and its better known variant UML state diagram [Object Management Group,
since 1997]. For instance, consider the decision logic of w11 a in Figure 1.(d). Without
involving too much technical details, Figure 3 shows an abstract model of the logic.
It illustrates several features we will develop in this project: (1) behavior hierarchy.
For example, at the top level of Figure 3 there are two states: working and failed.
The logic for ordering and distribution is further defined underneath the working state.
Similarly the details of the ordering decision logic is given underneath its representing
state. Behavior hierarchy provides a multi-level abstraction of the decision logic of an
element. A designer or a modeler can focus on part of the logic (s)he is interested in at
the right level of granularity; (2) behavior composition, which is implemented as parallel
compositions of sub-states. Each sub-state represents an independent unit of logic. In
Figure 3 the working state of w11 a contains two parallel sub-states: one state representing
ordering logic and the other representing distribution logic. Behavior composition allows
a component of a decision logic to be developed and tested independently before being
integrated with others.
(c) Improve model reusability. As part of our goal for this project, we want to develop
methods and a tool that can be used for modeling and analyzing practical stochastic
supply chains. As a principle in software engineering, code (and model) reuse not just
helps productivity by reusing existing code (and model), it also helps improve quality
of code (and model) by focusing verification and validation activities on reusable components.
Using type theory developed in programming languages, we will develop an
object-oriented model-reuse technique for agent-based supply-chain modeling. Figure 4
shows a draft of the object-oriented type hierarchy we will use to support model reuse.
The type hierarchy supports model reuse by abstracting common behaviors of similar
elements to a super class. For instance, class agent summarizes behaviors and attributes
essential to all types of supply-chain elements. Class node is a special case of agent but it
contains behaviors and attributes common to all the facility nodes, including suppliers,
warehouse, and retailers.
(d) Support stochastic modeling and decision optimization. In [Tan and Xu, 2008] we extended
Markov decision processes to model stochastic behaviors of an element of a supply
chain. For example, transitions between a working state and a failed state have optional
probability labels. These labels define the failure and recovery probabilities of an element.
In this project, we propose to extend our previous work with the capability of
decision optimization. Specifically we will combine the rewards mechanism defined in
Markov decision processes with probabilistic model checking technique. The combined
approach can find the optimal decision for an element of a stochastic supply chain under
a given scenario.
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