The Supply Chain Management
Game for the Trading Agent
Competition 2004
Supervisor: Ishai Menashe
Dr. Ilana David
final presentation: 10-Oct-04
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Outline
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Game overview
Motivation
Related issues
Challenges posed ahead
Solution outline
High level design of the system
Communication protocols
Algorithmic ideas
Performance Report
Ideas for future enhancements
References
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Game overview
In the TAC SCM scenario, 6 agents representing PC
assemblers that operate in a common market
environment and compete for customer orders and
for procurement of a variety of components, over a
period of several months.
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Illustration of a TAC day, where the agent plans,
produces and delivers PCs.
The agent must make
several decisions each day:
1.
2.
3.
4.
5.
What RFQs to issue for
components to suppliers.
Which suppliers’ offers to
accept.
What PCs to manufacture.
Which customer orders to
ship.
Which customers’ RFQs to
respond to and with what
offers.
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Motivation
Effective SCM is vital to the
competitiveness of manufacturing
enterprises. It impacts their ability
to meet changing market demands
in a timely and cost effective
manner.
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Related issues
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Dynamic programming and control
Feedback control
Distributed and parallel programming
AI – decision making and search Alg
Optimization
Supply chain models
Non-cooperative game theory
Computational learning theory
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Challenges posed ahead
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Uncertainty and incomplete information
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Dynamic environment
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Strategic behavior
“The game is far too complex to solve analytically or
characterize optimal behavior, due largely to the
issues of uncertainty, dynamism, and strategy...”
Distributed Feedback Control for Decision Making on Supply Chains - University of Michigan
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Our work process
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preparation stage
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forming our solution’s design
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implementation stage
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experiments and participating in the
competition
analysis and conclusion
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Solution outline
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Decomposition of the problem
Strategic policy adapts dynamically
Sharing aggregated environment
parameters
Feedback mechanism
Coordinating by shared purpose
my _ decisions t  hagent ( f sales ( statet ), f procurment ( statet ), f factory ( statet ))
statet 1  g agent (my _ decisions t , external _ inputs, statet )
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High level design of the system
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Functional distribution
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Sales module – interacts with customers and makes PCs’
offers
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Procurements module – interacts with suppliers and
handles biding for components
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Factory module – controls manufacturing and shipping
schedule
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Physical distribution architecture communicating
using RMI
Separating data gathering and functionality
Access to updated data using Multiple Reader One
Writer model
Using given functionality within the SCMAgent
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Agent deployment
FactoryModule
ProcurementModule
SalesModule
Agent + State
TacGameServer
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Static structure
«interface»
Remote
«interface»
ISalesModule
«interface»
IProcurementModule
«interface»
IFactoryModule
SalesModule
ProcurementModule
FactoryModule
ICurrentStat
IFutureState
IState
«interface»
IOurAgent
«interface»
IRemoteSCMAgent
Stat
OurAgent
RemoteSCMagent
UnicastRemoteObject
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Sequence diagram
GameServer
OurAgent
ProcurementModule
OffersSelectorThread
State
Suppliers Offers
Update State
Update Module
End Of Messages Notification
Get Relevant State Parameters
Timer
End Of Day
End Of Day Notofication
Return Decision
Return Decision
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Loop schema executed by all the
threads each day
1.
2.
3.
4.
Wait until End of Messages Notification.
Compute the decision according the data
received from OurAgent. This computation
should be finished X time units before the
end of the day.
X time units before the end of the day,
End of Day notification is received. The
thread sends its computation result to the
OurAgent object.
If not end of game go to 1.
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Communication schema of OurAgent
1.
2.
3.
4.
5.
When receiving new data update the State
and the modules.
When receiving the SimulationStatus:
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Start a timer for the current day.
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Update state parameters.
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Generate the EndofMessages notification.
X time units before the end of the day
generate the EndofDay notification.
When receiving a decision from one of the
threads, deliver it to the game server.
When the game is over, reset the State
instance and the data on the different
modules.
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Decision-maker threads behavior
End Of Messages
Wait For End of
Messages
Calculate
Decision
Finish Calculate Decision
End Of Day
Decision Ready
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OurAgent objects behavior
Sending /
Receiving
Current Day’s
Messages
Fire End Of Messages
Waiting For
Decisions
Fire End Of Day
All Decisions Sent
Sending /
Receiving
Decisions
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Algorithmic ideas
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approximating State parameters.
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refinement of the equilibrium by expanding
activity.
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simple greedy algorithm:
1. Collecting relevant parameters.
2. the weighted average of these
parameters gave us a total score.
3. the objects were sorted from the best to
the least valuable one.
4. we used as many objects as we could,
base on the constraints of the problem
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Performance Report
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In the qualifying rounds we
finished in place 28 with an
average of -11.64M, after
playing 78 games.
In the seeding rounds we
finished in place 28 with an
average of -37.9M, after playing
76 games.
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Results
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Ideas for future enhancements
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Refinement of the feed back process at
each module, throughout each day, using
iterations on the state of the game
effected by other modules decisions
before performing these decisions.
Decreasing communication overhead in
the distributed deployment, by
transferring functionality to distant
modules computers.
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References
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Raghu Arunachalam; Norman Sadeh; Joakim Eriksson; Niclas
Finne; Sverker Janson – “The Supply Chain Management Game
for the Trading Agent Competition 2004”
Christopher Kiekintveld; Michael P. Wellman; Satinder Singh;
Joshua Estelle; Yevgeniy Vorobeychik; Vishal Soni; Matthew
Rudary – “Distributed Feedback Control for Decision Making on
Supply Chains”
Philipp W. Keller; Felix-Olivier Duguay; Doina Precup – “RedAgent
- Winner of TAC SCM 2003“
Joshua Estelle; Yevgeniy Vorobeychik; Michael P. Wellman;
Satinder Singh; Christopher Kiekintveld; Vishal Soni – “Strategic
Interactions in a Supply Chain Game”
Michael Benisch; Amy Greenwald; Victor Naroditskiy; Michael
Tschantz – “A Stochastic Programming Approach to Scheduling in
TAC SCM”
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