David Pardoe

Peter Stone

The University of Texas at AustinDepartment of Computer Sciences

TacTex-05: A Champion Supply Chain Management Agent

Supply Chain Management

• Research goal: automate the process• Trading Agent Competition (TAC SCM)• Many challenges• TacTex-05 (2005 winner) - agent composed of

several interacting components:– prediction

– optimization

– adaptation

Outline

• Summary of TAC SCM

• TacTex-05 agent design

• Adaptive aspects of TacTex-05

• Competition results and experiments

• Conclusion

TAC SCM

• Agents compete as manufacturers

• 220 simulated days per game (15s each)

Component Procurement

• Supplier’s production capacity fluctuates

• Prices depend on supplier’s free capacity

Customer Negotiation

• 16 computer types in 3 segments

• Daily number of RFQs fluctuates

Factory Scheduling

• Limited production capacity

• Daily storage cost for all inventory

Outline

• Summary of TAC SCM

• TacTex-05 agent design

• Adaptive aspects of TacTex-05

• Competition results and experiments

• Conclusion

Demand Model

• Goal: predict future customer demand

• Bayesian approach adapted from DeepMaize (Kiekintveld et al. 2004)

Order Probability Predictor

• Want to predict P(order | offer price)

• Linear predictor for each computer type

Demand Manager

• Given resources and predictions, determine:– production schedule– deliveries– offers on all of today’s RFQs

• All done with greedy scheduling algorithm

Supplier Model• Estimate each supplier’s free capacity from offers• Use estimates to predict future offer prices

Supply Manager: What to Order

• Goal: maintain a threshold inventory

Supply Manager: When to Order

• Given a desired delivery, when to send RFQ?

• Assume today’s price pattern holds

Outline

• Summary of TAC SCM

• TacTex-05 agent design

• Adaptive aspects of TacTex-05

• Competition results and experiments

• Conclusion

Adaptation

• Different opponents lead to different situations

• Adapt by modifying predictions

• Make use of game logs

Two Areas of Adaptation

• Initial orders and endgame sales

• Important, but difficult to reason about

• Agents may handle as special cases

• Update predictions during these periods

Outline

• Summary of TAC SCM

• TacTex-05 agent design

• Adaptive aspects of TacTex-05

• Competition results and experiments

• Conclusion

Final Results

• Adaptation important:– ordered 95,000 components on first day

– SouthamptonSCM: 22,000; Mertacor: 18,000

Experiments

• Experiments analyzing agent components

• Use TAC Agent Repository

• Compare modified versions of TacTex-05

• Test adaptation against different opponents

Results

• Start-game adaptation– competition results very atypical

• End-game adaptation– beats fixed strategies in experiments

• Predictive models:– supplier price predictions most important

• Often better to wait to order components– tradeoff: price vs demand certainty

Outline

• Summary of TAC SCM

• TacTex-05 agent design

• Adaptive aspects of TacTex-05

• Competition results and experiments

• Conclusion

Related Work

• Many TAC SCM agent descriptions– SouthamptonSCM – He et al. 2006– Mertacor – Kontogounis et al. 2006– DeepMaize – Kiekintveld et al. 2006– CMieux – Benisch et al. 2006

• Available from TAC website http://www.sics.se/tac

TAC News

• 2006 TAC SCM competition complete– Won by TacTex-06– Most important addition: use learning to

predict future changes in computer prices

• TAC in 2007: 3 games– TAC Classic– TAC SCM– New market design game

Conclusion

• Introduced TAC SCM• Described TacTex-05

– prediction– optimization– adaptation

• Future work– additional learning, adaptation– focus on component price prediction, ordering

Thank You!