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

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(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. 8
Working [!AllSuppilersAvail] Ordering Decision Logic One-supplier Strategy Order Splitting Strategy [supplier=s a ] Decide supplier [supplier=s b ] Compute orders Order from s a Order from s a e clock Compute order Compute order Place order e clock e clock Place order Place order [AllSuppilersAvail] Distribution Decision Logic Check inventory e clock Compute Distribution Distribute Failed Figure 3: A component-based design for the decision logic of warehouse w a 11 . 9
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Page 3 and 4: 1 Overview and objectives Project D
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