Adaptive strategies for predicting bidding prices in supply chain management

ABSTRACT

Supply Chain Management (SCM) involves a number of interrelated activities from negotiating with suppliers to competing for customer orders and scheduling the manufacturing process and delivery of goods. Decision support systems for SCM need to be able to cope in uncertain, complex and highly competitive environments. Supporting dynamic strategies is a major but unresolved issue in the area. In this paper we examine two different approaches to address the issue of predicting customer offer prices that could result in orders in the domain of supply chain management. The first approach is to model the competitors' behaviour and predict their bidding prices according to the evolved models. The second one is to predict the lowest order prices for products for a number of days in the future using the time series of these prices. A set of algorithms are implemented based on Genetic Programming and Neural Networks learning techniques. The algorithms are tested in the TAC SCM simulated environment and the results are compared in terms of accuracy of prediction and execution time. Both learning techniques showed the potential for predicting prices in competitive and dynamic environments. The proposed Neural Networks algorithms demonstrate slightly better performance when tested in the TAC SCM environment compared to the algorithms implemented using Genetic Programming learning technique.

References

1. Benish M., Andrews, J., and Sadeh, N. 2006. Pricing for Customers with Probabilistic Valuations as a Continuous Knapsack Problem. In Proceedings of the Eighth International Conference on Electronic Commerce (Fredericton, Canada, 2006). ICEC-06, 38--46. DOI= http://doi.acm.org/10.1145/1151454.1151475
2. Benish M., Andrews, J., Sardinha, A., and Sadeh, N. 2006. CMieux: Adaptive Strategies for Competitive Supply Chain Trading. ACM SIGecom Exchanges, 6, 1 (June, 2006). ACM Press, New York, NY, USA, 1--10. DOI= http://doi.acm.org/10.1145/1150735.1150737
3. Benisch, M., Greenwald, A., Grypari, I., Lederman, R., Naroditskiy, V., and Tschantz, M. 2004. Botticelli: A supply chain management agent. In Proceedings of the Third International Conference on Autonomous Agents and Multi-Agent Systems (New York, NY, July 19--23, 2004). AAMAS-04, 1174--1181.
4. Collins, J., Arunachalam, R., Sadeh, N., Eriksson, J., Finne, N., and Janson, S. 2006. The Supply Chain Management Game for the 2007 Trading Agent Competition. Technical Report CMU-ISRI-07-100. Carnegie Mellon University.
5. Dahlgren, E. and Wurman, P. 2004. PackaTAC: A conservative trading agent. ACM SIGecom Exchanges, 4, 3. ACM Press, New York, NY, USA, 38--45. DOI= http://doi.acm.org/10.1145/1120701.1120707
6. Dong, R., Tai, T., Yeung W., and Parkes D. C. 2004. HarTAC -- the Harvard TAC SCM '03 Agent. In Proceedings of the Trading Agent Design and Analysis Workshop (New York, NY, USA, 20 July, 2004). TADA-03, 1--8.
7. He, M., Rogers, A., Luo, X., and Jennings, N. R. 2006. Designing a Successful Trading Agent for Supply Chain Management. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems (Hakodate, Japan, 8--12 May, 2006). AAMAS-06, 1159--1166.
8. Ghani, R. 2005. Price prediction and insurance for online auctions. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (New York, NY, USA, 2005), 411--418.
9. Ketter, W., Collins, J., Gini, M., Gupta A., and Schrater P. 2005. Identifying and Forecasting Economic Regimes in TAC SCM. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (Edinburgh, Scotland, UK, 30 July -- 5 August, 2005), 53--60.
10. Keller, W., Dugay, F.-O., and Precup, D. 2004. RedAgent -- winner of the TAC SCM 2003. ACM SIGecom Exchanges, 4, 3. ACM Press, New York, NY, USA, 1--8. DOI= http://doi.acm.org/10.1145/1120701.1120703
11. Ketter, W., Kryznaya, E., Damer, S., McMillen, C., Agovic, A., Collins, J., and Gini, M. 2004. MinneTAC sales strategies for supply chain TAC. In Proceedings of the Third International Conference on Autonomous Agents and Multi-Agent Systems (New York, NY, July 19--23, 2004). AAMAS-04, 1372--1373.
12. Kiekintveld, C., Miller, J., Jordan, P. R., and Wellman, M. P. 2006. Controlling a supply chain agent using value-based decomposition. In Proceedings of the Seventh ACM conference on Electronic commerce, Ann Arbor, Michigan, USA, 208--217. DOI= http://doi.acm.org/10.1145/1134707.1134730
13. Kontogounis, I., Chatzidimitriou, K. C., Symeonidis, A. L., and Mitkas, P. A. 2006. A Robust Agent Design for Dynamic SCM Environments. In Proceedings of the Fourth Hellenic Joint Conference on Artificial Intelligence (Heraklion, Greece, May, 2006). SETN'06, 18--20.
14. Koza, J. 1992. Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.
15. Mitchell, T. M. 1997.Machine Learning. MIT Press and The McGraw-Hill Companies, Inc. International Edition.
16. Narahari, Y., Raju, C. V. L., Ravikumar, K., and Shah, S. 2005. Dynamic pricing models for electronic business. Sadhana, 30, 2--3, 231--256. DOI= http://doi.acm.org/10.1007/BF02706246
17. Pardoe, D. and Stone, P. 2007. Adapting in agent-based markets: A study from TAC SCM. In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (Honolulu, HI, May, 2007). AAMAS-06, 677--679.
18. Pardoe, D. and Stone, P. 2006. Predictive Planning for Supply Chain Management. In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling (Cumbria, UK, June, 2006). ICAPS-06, 21--30.
19. Pardoe, D. and Stone, P. 2004. Bidding for Customer Orders in TAC SCM: A Learning Approach. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (New York, NY, July 19--23, 2004). AAMAS-04, 52--58.
20. Sadeh, N. M., Hildum, D. W., and Kjenstad, D. 2003. Agent-based e-supply chain decision support. Journal of Organizational Computing and Electronic Commerce, 13, 3/4, 225--42.
21. Santini, M. and Tettamanzi, A. 2001. Genetic Programming for Financial Time Series Prediction. In Proceedings of the European Conference on Genetic Programming, (EuroGP'2001), 2038, Springer-Verlag, Berlin, 361--370.
22. Schapire, R. E. and Singer, Y. 2000. BoosTexter: A boosting-based system for text categorization. Machine Learning, 39, 2/3, 135--168.
23. Schapire, R. E., Stone, P., McAllester D., Littman M. L., and Csirik, J. A. 2002. Modelling auction price uncertainty using boosting-based conditional density estimation. In Proceedings of the Nineteenth International conference on Machine Learning (University of New South Wales, Sydney, Australia, July 8--12, 2002). ICML 2002, 546--553.
24. Schwaerzel, R. and Bylander, T. 2006. Predicting currency exchange rates by genetic programming with trigonometric functions and high-order statistics. In Proceedings of the Eighth Annual Conference on Genetic and Evolutionary Computation (Seattle, Washington, USA), 955--956. DOI= http://doi.acm.org/10.1145/1143997.1144167
25. Smith, K. A. and Gupta, J. N. D. 2000. Neural networks in business: techniques and applications for the operations researcher. Computers & Operations Research, 27, 11--12, 1023--1044.
26. Swaminathan, J. M., Smith, S. F., and Sadeh, N. M. 1998. Modeling Supply Chain Dynamics: A Multiagent Approach. Decision Science Journal, 29, 3, 607--632.
27. Wellman, M. P., Greenwald, A., and Stone, P. 2007. Autonomous Bidding Agents. Strategies and Lessons from the Trading Agent Competition. The MIT Press, Cambridge, MA.
28. Witten, I. H. and Frank, E. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo, CA.
29. Yao, J. T. and Tan, C. L. 2000. A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex. Neurocomputing. 34, 1--4, 79--98.

Index Terms

• Applied computing

  • Operations research

    • Decision analysis

• Computer systems organization

  • Architectures

    • Other architectures

      • Neural networks

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Neural networks

• Information systems

  • Information systems applications

    • Decision support systems