1 the supply chain management game for the trading agent competition 2004 supervisor: ishai menashe...

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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”