1 the supply chain management game for the trading agent competition 2004 supervisor: ishai menashe...
TRANSCRIPT
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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
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 makeseveral decisions each day:
1. What RFQs to issue for components to suppliers.
2. Which suppliers’ offers to accept.
3. What PCs to manufacture.4. Which customer orders to
ship.5. 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
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
Uncertainty and incomplete information
Dynamic environment
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
preparation stage
forming our solution’s design
implementation stage
experiments and participating in the competition
analysis and conclusion
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Solution outline
Decomposition of the problem Strategic policy adapts dynamically Sharing aggregated environment
parameters Feedback mechanism Coordinating by shared purpose
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_ ( ( ), ( ), ( ))
( _ , _ , )
t t t tagent sales procurment factory
t t tagent
my decisions h f state f state f state
state g my decisions external inputs state
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High level design of the system
Functional distribution Sales module – interacts with customers and makes PCs’
offers Procurements module – interacts with suppliers and
handles biding for components Factory module – controls manufacturing and shipping
schedule
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
TacGameServer
FactoryModule ProcurementModule SalesModule
Agent + State
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Static structure
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Sequence diagram GameServer OurAgent ProcurementModule OffersSelectorThread State
Suppliers Offers
Update State
Update Module
End Of Messages Notification
Timer
End Of Day
End Of Day Notofication
Get Relevant State Parameters
Return Decision
Return Decision
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Loop schema executed by all the threads each day
1. Wait until End of Messages Notification.2. Compute the decision according the data
received from OurAgent. This computation should be finished X time units before the end of the day.
3. 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.
4. If not end of game go to 1.
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Communication schema of OurAgent
1. When receiving new data update the State and the modules.
2. When receiving the SimulationStatus: Start a timer for the current day. Update state parameters. Generate the EndofMessages notification.
3. X time units before the end of the day generate the EndofDay notification.
4. When receiving a decision from one of the threads, deliver it to the game server.
5. 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 DayFinish Calculate Decision
End Of MessagesWait For End of
Messages Calculate Decision
Decision Ready
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OurAgent objects behavior
All Decisions Sent
Fire End Of Day
Fire End Of MessagesSending /Receiving
Current Day’s Messages
Waiting For Decisions
Sending / Receiving Decisions
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Algorithmic ideas
approximating State parameters.
refinement of the equilibrium by expanding activity.
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 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
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
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”