TacTex-05: A Champion Supply Chain Management Agent

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

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!