Abstract
We propose a technique for use in supply-chain management that assists the decision-making process for purchases of direct goods. Based on projections for future prices and demand, requests-for-quotes are constructed and quotes are accepted that optimize the level of inventory each day, while minimizing total cost. The problem is modeled as a Markov decision process (MDP), which allows for the computation of the utility of actions to be based on the utilities of consequential future states. Dynamic programming is then used to determine the optimal quote requests and accepts at each state in the MDP. The model is then used to formalize the subproblem of determining optimal request quantities, yielding a technique that is shown experimentally to outperform a standard technique from the literature. The implementation of our entry in the Trading Agent Competition-Supply Chain Management game is also discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arunachalam, R., Eriksson, J., Finne, N., Janson, S., Sadeh, N.: The supply chain management game for the trading agent competition 2004 (Date accessed: April 8, 2004), http://www.sics.se/tac/tacscm_04spec.pdf ,
Arunachalam, R., Sadeh, N.: The 2003 supply chain management trading agent competition. In: Rauterberg, M. (ed.) ICEC 2004. LNCS, vol. 3166, pp. 113–120. Springer, Heidelberg (2004)
Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)
Boutilier, C., Goldszmidt, M., Sabata, B.: Continuous value function approximation for sequential bidding policies. In: the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1999), Stockholm, pp. 81–90 (1999)
Boutilier, C., Goldszmidt, M., Sabata, B.: Sequential auctions for the allocation of resources with complementaries. In: The Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 1999), Stockholm, pp. 527–534 (1999)
Buffett, S., Grant, A.: A decision-theoretic algorithm for bundle purchasing in multiple open ascending price auctions. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 429–433. Springer, Heidelberg (2004)
Buffett, S., Scott, N.: An algorithm for procurement in supply chain management. In: Proc. of the Trading Agent Design and Analysis Workshop (TADA 2004), New York, pp. 9–14 (2004)
Byde, A.: A dynamic programming model for algorithm design in simultaneous auctions. In: Fiege, L., Mühl, G., Wilhelm, U.G. (eds.) WELCOM 2001. LNCS, vol. 2232, p. 152. Springer, Heidelberg (2001)
Howard, R.A.: Dynamic Programming and Markov Processes. MIT Press, Cambridge (1960)
Preist, C., Bartolini, C., Byde, A.: Agent-based service composition through simultaneous negotiation in forward and reverse auctions. In: Proceedings of the 4th ACM Conference on Electronic Commerce, San Diego, California, USA, pp. 55–63 (2003)
Priest, C., Byde, A., Bartolini, C., Piccinelli, G.: Towards agent-based service composition through negotiation in multiple auctions. In: AISB 2001 Symp. on Inf. Agents for Electronic Commerce (2001)
Puterman, M.L.: Markov Decision Processes. Wiley, Chichester (1994)
Shapiro, J.F.: Modeling the Supply Chain. Duxbury, Pacific Grove, CA (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Buffett, S. (2005). A Markov Model for Inventory Level Optimization in Supply-Chain Management. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_15
Download citation
DOI: https://doi.org/10.1007/11424918_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
eBook Packages: Computer ScienceComputer Science (R0)