TacTex-05: A Champion Supply Chain Management Agent
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Transcript of TacTex-05: A Champion Supply Chain Management Agent
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!