1. David Pardoe Peter Stone The University of Texas at Austin Department of Computer Sciences TacTex-05: A Champion Supply Chain Management Agent

2. 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

3. Outline Summary of TAC SCM TacTex-05 agent design Adaptive aspects of TacTex-05 Competition results and experiments Conclusion

4. TAC SCM Agents compete as manufacturers 220 simulated days per game (15s each)

5. Component Procurement Supplier’s production capacity fluctuates Prices depend on supplier’s free capacity

6. Customer Negotiation 16 computer types in 3 segments Daily number of RFQs fluctuates

7. Factory Scheduling Limited production capacity Daily storage cost for all inventory

9. Outline Summary of TAC SCM TacTex-05 agent design Adaptive aspects of TacTex-05 Competition results and experiments Conclusion

11. Demand Model Goal: predict future customer demand Bayesian approach adapted from DeepMaize (Kiekintveld et al. 2004)

12. Order Probability Predictor Want to predict P(order | offer price) Linear predictor for each computer type

13. Demand Manager Given resources and predictions, determine: –production schedule –deliveries –offers on all of today’s RFQs All done with greedy scheduling algorithm

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

18. Supply Manager: What to Order Goal: maintain a threshold inventory

19. Supply Manager: When to Order Given a desired delivery, when to send RFQ? Assume today’s price pattern holds

20. Outline Summary of TAC SCM TacTex-05 agent design Adaptive aspects of TacTex-05 Competition results and experiments Conclusion

21. Adaptation Different opponents lead to different situations Adapt by modifying predictions Make use of game logs

22. 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

23. Outline Summary of TAC SCM TacTex-05 agent design Adaptive aspects of TacTex-05 Competition results and experiments Conclusion

24. Final Results Adaptation important: –ordered 95,000 components on first day –SouthamptonSCM: 22,000; Mertacor: 18,000

26. Experiments Experiments analyzing agent components Use TAC Agent Repository Compare modified versions of TacTex-05 Test adaptation against different opponents

27. 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

28. Outline Summary of TAC SCM TacTex-05 agent design Adaptive aspects of TacTex-05 Competition results and experiments Conclusion

29. 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

30. 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

31. Conclusion Introduced TAC SCM Described TacTex-05 –prediction –optimization –adaptation Future work –additional learning, adaptation –focus on component price prediction, ordering

32. Thank You!