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

SimRisk: An Integrated Open-Source Tool for Agent-Based
Modeling, Parallel Simulation, and Formal Analysis and
Optimization of Large-Scale Supply Chains under Uncertainty
Principle Investigators:
Li Tan
School of Electrical Engineering and Computer Science, Washington State University
Shenghan Xu
College of Business and Economics, University of Idaho
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Project Summary
This grant provides funding for developing novel methodologies and an open-source tool for
modeling, simulating, and formally analyzing and optimizing large-scale supply chains under uncertainty.
With increasing integration of the world’s economy, the size and complexity of global
supply chains are rising rapidly, so is the reliance of our economy on these supply chains. Understanding
and optimizing stochastic behaviors of these large-scale global supply chains is crucial for
predicating and managing their uncertainty and risks. To attack computational challenges arising
from the scale and complexity of these global supply chains, this interdisciplinary research will seek
synergetic breakthroughs in modeling, analyzing, optimization, and implementation. Specific topics
will include: 1) developing an agent-based stochastic modeling framework, which will model the
elements of a supply chain as autonomous Markov decision processes. The agent-based framework
will facilitate the study of complex stochastic behaviors arising from interactions of these elements;
2) developing a parallel simulation technology using generative software engineering methods; 3)
developing an efficient and rigorous automated formal stochastic analysis and optimization technique,
which is based on probabilistic model checking techniques developed in computer science; 4)
developing an open-source tool to disseminate the technologies obtained in this project. Preliminary
study and tools development have shown the benefits of this interdisciplinary research.
Intellectual Merits If successful, this project will significantly improve existing supply-chain
stochastic analysis and optimization technologies and tools in terms of efficiency, scalability, accuracy,
and usability. Specifically, the agent-based stochastic modeling framework will extend
agent-based modeling with the ability of modeling stochastic behaviors of a supply chain. To improve
efficiency and scalability, the generative simulation technology will harness computational
power brought by recent advances on multi-core hardware and Peta-level computing platforms.
The formal analysis and optimization technique will preserve the accuracy of deductive proof but
will greatly improve its efficiency. The project will continue the interdisciplinary study started in
[Tan and Xu, 2008, 2009a], and extends probabilistic model checking technology to supply-chain
risk analysis. The project will produce an open-source tool as technology delivery platform, which
will empower practitioners to conduct intensive analysis of risks and other stochastic factors for a
global supply chain with the scale and the complexity far beyond the current technologies allow.
Broader Impact Technologies developed in the project will be published in leading journals and
major conferences in operations management and computer science. The majority of this grant will
be used to support a junior women faculty member and a Ph.D. student. Part of this research will
be built into the current and future courses on supply-chain management, mathematical modeling,
software engineering, and parallel computing in Washington State University and the University of
Idaho. Situated in two geographically adjacent rural areas, both universities have a long tradition
of supporting women students and students from underrepresented groups in their business and
engineering programs. Last but not least, the open-source tool will be freely available. Lecturers
may use it to help students develop intuition behind supply-chain risk management and try “whatif”
scenarios for assessing different risk management strategies. Practitioners can use it, for instance,
to analyze risks arising from interactions between different elements of a global supply chain and
identify deficiency in risk management plans. The analysis results will help companies improve the
security of their supply chains, and in a sense, strengthen the security of our economy as a whole.
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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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enefit of agent-based modeling is that it scales well for systems with large numbers of autonomous
elements (agents). Recently it draws much research interest in simulating complex
social systems[Bonabeau, 2002]. Analyzing complex stochastic supply chains falls into the
type of problems that an agent-based approach is prescribed for: although the stochastic behavior
of each element is relatively easy to understand, uncertainty arising from interactions
among these elements is not. Although there are some prior works on agent-based approach
for supply chains with deterministic behaviors [Swaminathan et al., 1998], little research has
been done on addressing stochastic aspect of supply chains using agent-based approach. Since
understanding stochastic behaviors of supply chains is crucial for supply-chain risk analysis,
an objective of this project is to fill this gap by studying agent-based modeling for supply
chains under uncertainty.
2. Generative parallel simulation technology, which simulates a supply-chain model by generating
executable simulation code. Recent advance in multi-core hardware and the emerge
of peta-level parallel computing platforms provide extra parallel computing powers for computers
ranging from desktops to super computers. To take full advantage of these parallel
architectures, our generative simulation technology will generate executable simulation code
optimized for these parallel architectures.
3. Formal stochastic analysis and optimization technique, which is derived from probabilistic
model checking technique developed in computer science. In contrast to stochastic simulation,
the formal stochastic analysis technique constructs a rigorous proof in a fully automated
manner. It provides mathematically sound stochastic analysis for supply-chain applications
more concerning the accuracy of analysis result. Developed for verifying stochastic system
designs, probabilistic model checking has made significant progress in past several years with
the development of efficient symbolic algorithms and open-source tools ([Hinton et al., 2006]).
To the best of our knowledge, in [Tan and Xu, 2008] we are the first to introduce probabilistic
model checking for supply chain risk analysis. In this project we will conduct an extensive
interdisciplinary study on applying model-checking-based technology to supply-chain analysis.
We will address issues including pattern-based problem formulation, proof extraction, and
result interpretation. In [Xu and Tan, 2009] we proposed a model-checking-based approach
for scheduling optimization. In this project we will extend this work to optimize operations
of supply chains under uncertainty.
Aforementioned theoretical study lays the foundation for Simrisk, the open-source tool we will
develop as a platform to delivery new technologies technology delivery platform. Specifically, we
will develop an agent-based visual modeling language as Simrisk’s front end. Simrisk will include
a code generator that can product simulation code in C/C++ targeted for specific parallel platforms.
Simrisk will also have a formal analysis and optimization module that allows a user to
formally specify stochastic properties of interest and provides a push-button approach to analyze
and optimize stochastic supply chains with respect to these properties. The design of Simrisk
will emphasize on extensibility. Simrisk is not only an implementation of modeling and analysis
technologies developed in this project, but also an open platform which users may extend with
new functionalities. The tool will be built on Eclipse, a leading open-source software development
environment [the Eclipse Foundation, since 2004]. Eclipse is known for its extensibility: users can
extend its functionalities with customized plugins. We will define an interface which allows third
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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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3 Background
3.1 Related Works
Existing stochastic analysis techniques for supply chains include stochastic simulation, deductive
proof, and stochastic programming. Stochastic simulation works by simulating operations of a supply
chain under different sceneries and then applying statistic methods to the simulation results.
Stochastic simulation is widely used for a variety of applications such as risk analysis and performance
evaluation (cf. [Kleijnen, 2005]). Although stochastic simulation generally scales well, it is
also prone to statistic errors; in deductive proof, one can prove stochastic properties of a supply
chain using an array of methods, such as Bayesian networks, Fuzzy logics, and Hybrid networks
(cf. [Pai et al., 2003]). Deductive proof requires a strong theoretical background. Since deductive
proof is often done by hand, it is not suitable for large-scale supply chains; stochastic programming
approach models the design of a supply chain with uncertainty as an optimization problem. For
example, MirHassani et al. [2000] considered an integer stochastic programming (ISP) representation
and solution for a two-stage design of a supply chain. The first stage concerned decisions on
opening/close of facilities and their capacity levels before future demands were realized. The second
stage involved production and distribution plans during supply-chain operation. The authors
used Benders decomposition to solve stochastic programming programs for supply chain networks
involving up to 8 manufacturing sites, 15 distribution centers, 30 retailers, and with 100 scenarios.
Alonso-Ayuso et al. [2003] also studied two-stage stochastic supply chain using stochastic programming
and proposed an algorithm based branch-and-fix heuristic. They tested their algorithm on
networks up to 6 manufacturing sites, 12 products, 24 market regions, and with 23 scenarios. Santoso
et al. [2005] noticed the limitation of these techniques in terms of scalability. They proposed a
solution technique that leveraged the strength of statistic simulation. They developed a sample average
approximation schema (SAA) to approximate expectations in the objective function and then
applied an accelerated Benders decomposition problem to solve the rest of stochastic programming
problem. Although their approach could handle much bigger supply chain design problems, it also
suffered from the similar drawback of stochastic simulation: the accuracy of its result was prone
to statistic errors. Additionally the application of stochastic programming was limited to design
optimization, whereas requests for stochastic analysis may come from many other applications, for
instance, analyzing an existing design of a supply chain under “what-if” scenarios.
In this project we will develop an agent-based modeling framework as the front-end of the
analysis tool. Swaminathan et al. [1998] studied a multi-agent approach for modeling supply-chain
dynamics. As with us, they believed a multi-agent modeling framework is “a natural choice”
for modeling supply chains because “supply-chain management is fundamentally concerned with
coherence among multiple decision makers”. They modeled structural elements as agents, which
interacted with each others using control elements. Our agent-based research follows the same
line but we will extend it with stochastic modeling using Markov decision processes. Additionally
we will provide formal syntax and semantics to remove ambiguity in model interpretation and to
facilitate rigorous formal analysis.
Our formal analysis technique will be based on probabilistic model checking [Bianco and de Alfaro,
1995]. In recent years the scalability and efficiency of probabilistic model checking was drastically
improved with the development of symbolic techniques such as Multi-Terminal Binary Decision
Diagrams (MTBDD) [Wang and Kwiatkowska, 2005]. Probabilistic model checkers such as PRISM
[Hinton et al., 2006] have been successfully applied to large-scale complex systems in biological
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processes [Kwiatkowska et al., 2008] and Randomised distributed algorithms [Kwiatkowska and
Norman, 2002]. We will use PRISM as the underly decision procedure for formal stochastic analysis.
We will develop a pattern-based method for a user to specify stochastic properties of a supply
chain. We also will extend the decision algorithm in PRISM so we can extract the proof from a
model-checking run and develop a game-theoretic approach to interpret the proof to an end user.
On the implementation side, Rossetti et al. [2006] used an objective-oriented framework to
implement a Java package for supply-chain simulation. Liu et al. [2004] introduced a Java-based
supply-chain simulation tool Easy-SC. In Easy-SC modeling environment, facilities were modeled
as instances of pre-defined six enterprise nodes, and routes were implemented as connection arcs.
Wang et al. [2008] discussed a general business simulation environment (GBSE) developed by IBM
China research lab. GBSE was a Java-based event-driven simulation tool built on top of the
Eclipse platform [the Eclipse Foundation, since 2004]. Our proposed open-source tool will also be
developed on Eclipse for better extensibility. In contrast to GBSE’s conventional implementation
of a simulator, in which the simulation logic is integrated with the tool, our tool will implement
a generative simulation engine, which will generate stand-alone simulators tailored for targeted
hardware architectures.
3.2 Preliminary Study
To demonstrate benefits and feasibility of our ideas, we have conducted a preliminary study on
components of the proposed research.
In [Tan and Xu, 2008] we proposed a model-checking-based formal stochastic analysis framework
for supply chains. The framework used an extension of Markov decision processes to model
elements of a supply chain. We encoded stochastic properties of a supply chain in the Probabilistic
Computational Tree Logic (PCTL). To the best of our knowledge, this was the first published
work on applying probabilistic model checking technique to supply chain analysis. In [Tan and
Xu, 2009a] we further studied the benefits of the formal stochastic analysis by using it to analyze
risks in supply chain consolidation. We conducted a quantitative analysis of the impact of four
consolidation strategies on risks in a 4-echelon supply chain. Figure 1 shows the configurations of
supply chain networks used in the experiments in [Tan and Xu, 2009a]. Figure 1.(a) shows two independent
networks. Figure 1.(b) shows the consolidated supply chain resulting from a merger and
acquisition with product pooling strategy. Figure 2 reports a result of formal stochastic analysis. It
shows the probability of on-time deliveries under different supply-chain consolidation strategies. In
[Tan and Xu, 2009b] we tested an implementation of agent-based stochastic modeling approach for
supply chains. The implementation included an object-oriented type system for improving model
reusability. Additional results from our preliminary study were also presented in [Xu and Tan,
2008, Tan and Xu, 2009c].
4 Relations to the principal investigators’ long-term goals
The long term goal of this research is to advance modeling and automated analysis technologies
for analyzing stochastic and temporal behaviors of large-scale supply chains. The proposed project
represents a significant step towards this long-term goal. Our preliminary study has shown the
feasibility and benefits of every technological components in this proposed research, including agentbased
stochastic modeling [Tan and Xu, 2008], generative simulation technology [Tan and Xu,
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s a
s b
u a1 /d a1
w11 a w11
b u w12 a w11
b b1 /d b1 u a1 /d a1 u b1 /d b1
w21 a w21
b u b2 /d b2
u w22 a w22
b u b2 /d b2
w23 a w23
b u b2 /d b2
w24 a w24
b a2 /d a2
u a2 /d a2
u a2 /d a2
u a2 /d a2
u b2 /d b2
r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14 r 15 r 16
(a) Two supply chains operated separately by companies A and B
s a
s b
u a1 /d a1
w11 a w11
b u w12 a w11
b b1 /d b1 u a1 /d a1 u b1 /d b1
w21 a w21
b u b2 /d b2
u w22 a w22
b u b2 /d b2
w23 a w23
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u a2 /d a2
u a2 /d a2
u b2 /d b2
r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14 r 15 r 16
(b) A consolidated supply chain with product pooling.
Figure 1: Supply chain configurations before and after a merger. d qi and u qi are the failure and
recovery rates of a level-i warehouse operated by q ∈ {a, b} [Tan and Xu, 2009a].
L-2 Cross-Warehouse
L-1 Cross-Warehouse
Product pooling
Improvement
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Figure 2: Improvement of probability of on-time delivery with different supply-chain consolidation
strategies.
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2009b], and model-checking-based formal analysis technique[Tan and Xu, 2009a]. We also developed
a prototype of a modeling and simulation tool for supply-chain analysis [Tan and Xu, 2009a].
These accomplishments have prepared us to take on this project. This grant will provide crucial
resources we need to advance and complete the research we started in our preliminary study. If
funded, this project will leverage the benefits of recent advances in computer science, especially
in software engineering and formal methods and apply them to stochastic supply-chain analysis.
The project will also advance theories and methods crucial for modeling and analysis of largescale
stochastic supply chains. Finally the project will produce an open-source tool Simrisk as a
platform for delivering new analysis and optimization technologies to practitioners and researchers.
Simrisk will also serve as an open platform and technology testbed that allows other researchers to
experiment new technologies. If successful, this project will empower researchers and practitioners
with technologies and an open-source tool that are scalable for analyzing large-scale global supply
chains under uncertainty. With this new capability, next we will apply Simrisk to supply-chain
risk management, contracting, and performance evaluation. As the follow-up of this project, we
will team with industrial leaders such as Boeing, which we already have collaboration with on
supply chain consulting, and research institutions such as Pacific Northwest National Lab (PNNL),
which we are collaborating with on high-performance simulation technology. We will apply the
methods and the tool produced in this project to the analysis of intercontinental supply chains
using Peta-scale computing platforms.
5 Research plan
5.1 Specific aim 1: develop an agent-based stochastic supply-chain modeling
framework
The framework will model elements of a supply chain as automatous stochastic agents. It will also
formally define operational semantics of these agents and their interactions. This is the start point
of this project since other activities rely on this model language as the front end. Specifically we
will address the following issues,
1. Agent modeling. This activity will consider how to model supply chain elements as autonomous
agents. Since our emphasis is on stochastic behaviors of these elements and each
element has its own decision logic, we will model agents as Markov decision processes. In [Tan
and Xu, 2008] we proposed an extension of Markov decision process (EMDP) and modeled
each element as a restricted two-state EMDP. For example, every element in a 4-echelon supply
chain in Figure 1.(a) has only two states: working and failed. In this project, we plan to
lift such restriction. We will encode more complex decision logic for each element. Moreover,
we will make the following advances in agent modeling for supply chains:
(a) Extend E-EMDP (Element Extended Markov Decision Process) in [Tan and Xu, 2008] to
include logic that models the decision process of an element. For instance, for warehouse
w11 a in Figure 1.(a), the proposed extension will also support the encoding of its ordering
and distribution logic. The ordering logic of w11 a will decide when and where to place
orders based on factors including w11 a ’s inventory, the pricing structures of its suppliers
s a and s b , and requests from its customers w21 a and wa 22 .
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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.
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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 .
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Figure 4: An object-oriented type hierarchy for model reuse.
5.2 Specific aim 2: develop high-performance generative simulation technology
Simulation still plays an important and indispensable role in the practice of supply chain
management. For this reason, we will retain simulation as part of our integrated stochastic
analysis framework but will greatly improve its performance. Much of existing research on
supply-chain simulation focuses on simulation algorithms and software implementation (cf.
[Terzi and Cavalieri, 2004, Kleijnen, 2005]). In this research, our interest on simulation is to
improve its efficiency and scalability by providing a better integration between software and
hardware. Our research is motivated by recent advances in multi-core architecture and Petalevel
computing platforms. These advances provide extra computation power for computers
ranging from desktops to super computers. A research question is how to harness these
powers to improve the speed and scalability of supply-chain simulation. Our solution is to
develop a reconfigurable generative simulation approach for supply-chain analysis. For each
supply-chain model, the proposed approach will on-the-fly generate simulation code that can
take advantage of a targeted computer architecture. Specifically we will develop a generative
simulation engine that can be reconfigured for two different types of architectures: multicore
personal computers and high-performance clusters. For a desktop computer with a
single multi-core processor, the engine will generate a multi-thread program. Each thread
represents an agent using the IEEE POSIX thread model [IEEE and the open group, 2004].
For a cluster of multi-core processors, the generative simulation technology will generate a
set of POSIX threads for each processor, and different sets of threads will communicate via
Message Process Interface (MPI).
A central issue in developing generative simulation engine is load balancing. That is, given
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a hardware architecture and an agent-based model, how to distribute threads and processes
to cores and processors for better performance. This issue has a special meaning in a cluster
environment: a multi-core cluster supports both shared-memory and message-passing communication,
which have very different characteristics. We will study optimal distribution
of threads and processes for minimizing communication overhead in context of agent-based
supply-chain simulation. Specifically we will study the following methods:
(a) Explore model structure and data dependency to improve load balancing. For example,
consider the supply chain in Figure 1.(b), and assume that we will run simulation on
a cluster of quad-core processors, whose architecture is shown in Figure 5. Figure 6
shows a distribution of threads and processes using heuristics from model structure
and data dependency. The communication between supply-chain elements is through
shipments and messages. We assume that messages can only be passed along routes.
As a general principle, threads for a sub-network of closely coupled elements will be
placed on the cores of the same processor. These closely coupled elements require more
frequent communication between them, which shall be implemented with less overhead
using shared memory. As an example, in Figure 6 threads for the elements of the
sub-networks of w21 a and wb 21 are assigned to the same processor, and processes for the
sub-networks of s a and s b are allocated to different processors. In general, the higher
elements are in a network hierarchy, the less they will communicate with each other, since
the operations of elements on a higher level will be planned over a much bigger planning
horizon. We reserve shared memory for communication among closely coupled low-level
elements and use message passing for communication among high-level elements.
(b) Profile threads and optimize thread scheduling. To further improve the performance
of generated parallel simulators, we will profile the execution time and the overhead of
threads, and use this information to optimize thread scheduling. Using the result from
profiling, the generative simulation engine will express the thread scheduling problem as
a linear programming problem. It will use the optimization result to define scheduling
policy for threads.
11

Server 1/Core 3
Server 2/Core 3
s a
s b
u a1 /d a1
w11 a w11
b u w12 a w11
b b1 /d b1 u a1 /d a1 u b1 /d b1
u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2
w a 21 w b 21
w a 22 w b 22
w a 23 w b 23
w a 24 w b 24
r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14 r 15 r 16
Server 1/Core 1 Server 1/Core 2 Server 2/Core 1 Server 2/Core 2
Figure 6: A distribution of threads for simulating the model in Figure 1.(b) on the cluster in Figure
5.
5.3 Specific aim 3: develop efficient formal stochastic analysis technique for
supply chains
Model checking [Clarke et al., 1986] is an automated verification technique in which the specification
of a system is formally encoded in a temporal logic. Model checking provides a “push-button”
approach to algorithmically verify a system on a temporal property and produce a mathematically
sound proof for its finding. Temporal logics used in model checking allow precise formulation of
subtle temporal properties. Model checking technology has been actively investigated and heavily
invested in industry and academia. Over years new techniques such as Binary Decision Graphbased
model checking [Burch et al., 1992] and SAT-based bounded model checking [Biere et al.,
1999] have drastically improved the scalability of model checking technology. Today model checking
has been successfully used for verifying industrial-size processor designs [Gerth, 2001] and software
applications [Ball et al., 2004]. Recently probabilistic model checking gains momentum by the
introduction of new symbolic techniques [Wang and Kwiatkowska, 2005]. It has been successfully
used to analyze a wide range of applications such as biological processes [Kwiatkowska et al., 2008]
and Randomised distributed algorithms [Kwiatkowska and Norman, 2002].
In this project we will develop a formal stochastic analysis technique derived from probabilistic
model checking. Particularly we will focus on the following issues in applying probabilistic model
checking to stochastic analysis of supply chains:
1. Pattern-based stochastic problem formulation. Problem formulation is the first step towards
formal stochastic analysis of supply chains. Probabilistic model checking formulates stochastic
properties in a probabilistic temporal logics. Using temporal logics requires some knowledge in
formal methods and may pose a hurdle for the adoption of new model-checking-based analysis
technique. To ease such difficulty, we propose a two-step process for problem formulation:
we will provide patterns for practitioners in supply-chain management to define stochastic
properties of interests in a language closer to their background, and we will develop an
algorithm that can translate a pattern-based representation of stochastic property to one in
a temporal logic.
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2. Proof extraction and game-based result interpretation. Probabilistic model checking analyzes
a system on a stochastic property by constructing a mathematical proof. Such proof carries
rich information about the system with respect to the stochastic property. For instance, in
context of supply-chain analysis, a proof can be used to understand analysis result and to
debug a supply-chain design. Furthermore, probabilistic model checkers like PRISM [Hinton
et al., 2006] answers the maximal (or minimal) probability in which a stochastic property may
hold on a supply chain. A proof can be used to formulate an optimal solution that reaches
such maximal (or minimal) probability.
Current research on extracting proofs from probabilistic model checkers is still limited because
of the complexity of these proofs. Existing work (cf. [Han et al., 2009]) emphasized on
formats of proofs. More research is needed on how to extract these proofs from existing
model checking algorithms and interpreting them to end users. In this project, we will study
the problems of proof extraction and result interpretation in context of supply chain analysis.
We will extend Tan and Cleaveland [2002]’s previous works on extracting proofs from a
traditional model checker to the domain of probabilistic model checking. Previously Tan
[2002] also developed a generic game-theoretic framework for interpreting proofs from various
automated verification procedures. The framework worked by playing an interactive game
with a user. By making decisions during the game, the user could choose part of the proof
(s)he wanted to explore. Game-theoretic approach is especially suitable for explaining proofs
with branching logics, including proofs from formal stochastic analysis of supply chains. Such
proofs are (potentially infinite) decision trees extended with probabilities and rewards. In
this project we will develop a game-theoretic approach that allows an end user to explore
proofs constructed by a probabilistic model checker.
5.4 Specific aim 4: implement an open source tool for knowledge dissemination
and technology transfer
The purpose of this research is to enable modeling and automated stochastic analysis technology
that are efficient and scalable for real-world large-scale supply chains. Besides the project’s proposed
technological advances, the success of the project also largely depends on how effective we can
disseminate knowledge and transfer technology to other researchers and practitioners. An opensource
tool will be an excellent vehicle for this purpose: it allow practitioners to try out new
technology and integrate the new tool to his/her existing workflow. It also provides an open
platform for researchers to test and extend new technology.
Our team members have successfully developed a variety of open-source tools including Concurrent
Workbench [Cleaveland et al., 2000], M 2 IST [Tan, 2006], and most recently Simrisk [Tan
and Xu, 2009b]. We will apply these experience and skills to this project. Specifically we will build
the tool on Eclipse [the Eclipse Foundation, since 2004]. Eclipse is a popular open-source software
development platform. Its model-based design frameworks EMF and GMF reduce the time and
the cost for implementing an agent-based modeling framework. Eclipse supports several code generation
frameworks including JET and Acceleo that can be used for building the reconfigurable
generative simulation engine. Finally Eclipse adopts an open architecture. Other researchers can
integrate their own analysis algorithms to the tool.
13

Q1/2010 Q2/2010 Q3/2010 Q4/2010 Q1/2011 Q2/2011 Q3/2011 Q4/2011 Q1/2012 Q2/2012 Q3/2012 Q4/2012
Agent-based Stochastic Modeling
Extension of Markov Decision Processes
Component-based design
Model reusability
Support for decision optimization
Re-targetable generative simulation engine
Load balance based on structure
information
Thread-scheduling optimization
Formal stochastic analysis
Pattern-based problem formulation
Proof extract and game-based
interpretation
Open source tools development
Table 1: The project timeline.
5.5 Project timeline
Table 1 gives the project timeline. The project will be carried out in a three-year period with efforts
of two faculty members, one Ph.D. student, and one Masters student. Table 1 lists durations and
finish time of activities defined for each specific aim.
5.6 Project management
In this project Li Tan will lead efforts on developing high-performance reconfigurable simulation
technology, automated formal stochastic analysis technique, and the open-source tool Simrisk.
Shenghan Xu will lead efforts on agent-based stochastic modeling and the empirical study of new
technologies developed in this project. The University of Idaho and Washington State University
Pullman campus are connected by a 7-mile highway. During the course of this project we will hold
weekly research meeting between two groups.
6 Broader impact
Stochastic analysis of supply chains is important for 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]. We expect that the success of this project will advance
research on these application. Because of the practical significance of stochastic analysis
in supply-chain management, the project is also expected to assist companies who are interested
in streamlining their supply chains. In addition, we plan to promote knowledge dissemination
and technology transfer by publications, the distribution of an open-source tool, and educational
programs. Theories and methods developed in this project will be shared in research community
14

via publications in leading journals and major conferences in operations management and computer
science. The open-source tool will be free available for practitioners who want to try out
new technologies and incorporate them to their existing workflow. Built on the leading software
development framework Eclipse, the tool will also provide an open framework for researchers and
engineers who want to contribute and/or integrate new stochastic analysis technologies.
Part of grant will be used to support a women faculty member (Shenghan Xu) for her interdisciplinary
in operations management and computer science. A significant amount of the grant
will be used to support a Ph.D student and a M.S. student. They will work with PIs to develop
underlying technologies. Part of this research will be built into the current and future courses
on supply-chain management, mathematical modeling, software engineering, and parallel computing
in Washington State University and the University of Idaho. Li Tan also leads a very active
undergraduate research program on software engineering in WSU, in which students help design
and develop a variety of research tools, including a preliminary implementation of Simrisk as
the proof of concepts. Through the development of these research tools, undergraduate students
gained value experience in software design, and they learnt skills essential to interdisciplinary study.
If funded, this project will contain a significant amount of undergraduate research activities. A
group of undergraduate students will work with PIs and graduate students on the design and the
implementation of Simrisk.
The Simrisk toolkit developed in this research will be also used in an upper-division undergraduate
class in supply-chain management in the University of Idaho. Its agent-based visual model
editing environment will help students develop intuition behind supply-chain risk management.
The tool will also allow students to try “what-if” scenarios for different risk management strategies
and learn how to optimize supply chains by balancing various factors.
15

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4

Budget and Justification
1 Salary
1.1 Personnel
The funding for this project will include the support for 1 Ph.D. student for 3 years and 1 masters
student for one academic year (9 months). The Ph.D. student will work with PIs to conduct basic
research on technologies required by the project, as defined in the research plan. The masters
student will assist the implementation of the open-source supply-chain modeling and stochastic
analysis tool. Training these students are also part of our efforts to broaden the impact of this
project by disseminating knowledge via graduate education.
The budget will include the summer support for Dr. Li Tan for 2.0 months per year and Dr.
Shenghan Xu for 1.0 month per year. With this support, PIs can spend time necessary for advising
students and conducting research as outlined in our research plan.
Faculty appointments at Washington State University and at the University of Idaho are generally
effective on an Academic Year (AY) beginning August 16. The summer support for Dr. Li
Tan (2.0 months per year) and Dr. Shenghan Xu (1.0 month per year) during the award will be
used during the summer period May 16 - August 15.
Graduate Research Assistants (GRAs) and Undergraduate Research Assistants (URAs) - Costs
are estimated based on calculations from the matrices issued by the Graduate School at Washington
State University.
• 1 - GRAs (PhD level - step 47) at $21,567.00 × 1 student = $21,567.00 (12 month appointment
at 50% FTE for three years of the award).
• 1 - GRAs (Master level - step 42) at $22,175.00 × 1 student = $22,175.00 (12 month appointment
at 50% FTE in the third year of the award).
1.2 Salary Increases
Based on Washington State University’s guidelines a 4% salary increase is calculated into the PI
and Graduate Research Assistants employed by Washington State University. All increases take
effect July 1st.
1.3 Fringe Benefits
Washington State University’s fringe benefits rates, as they apply to sponsored programs are as
follows: 32% for faculty and professional staff, 1.5% for graduate students.
1.4 Qualified Tuition Reduction
It is the policy of Washington State University to provide tuition for graduate research assistants
as a partial compensation for services. It is estimated for the academic year of 2010 that the QTR
will be $8,598= $8,598 based on Washington State University’s guidelines a 5% QTR increase is
calculated into each additional year.
1

2 Travel
The support for 2 trip per year for the first year, and 3 trips per year for the second and the
third years are requested. These funds will support PIs and graduate students to travel to related
conferences in operations management and computer science and present findings from this research.
3 Equipment and Software
The requested fund includes the expense for two laptop computers, two desktop computers, and an
entry-level server. Laptop and desktop computers ensure that every participant of this project has
adequate personal computing equipment to complete the proposed research and development work.
The entry-level server will be used to host the project for developing, maintaining, and distributing
the open-source tool. The requested fund will also include necessary software packages including
MPL and CPLEX (for performance comparison with stochastic programming), and Matlab.
4 Indirect Costs
Washington State University’s negotiated MTDC Indirect Cost rates with DHHD are 49.5% ”oncampus”
and 26% ”off-campus”. (Note: The MTDC bases consist of total costs less equipment
items in excess of $5,000 or more, minus each and any portion of a subcontractor over $25,000,
qualified tuition reduction).
5 Subaward
A subaward will be made to the University of Idaho ($79,720 over 3 year) for the portion of the
research that Dr. Shenghan Xu will conduct.
2