IJAT Vol.7 No.1 pp. 128-135
doi: 10.20965/ijat.2013.p0128


Flexible Multi-Layered Dynamic Supply Chain Models with Cooperative Negotiation

Yoshitaka Tanimizu, Chisato Ozawa, Yusuke Shimizu,
Buntaro Orita, Koji Iwamura, and Nobuhiro Sugimura

Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan

August 8, 2012
November 7, 2012
January 5, 2013
supply chain management, scheduling, genetic algorithm, multi-layer, negotiation
Supply chain management has been investigated for the configuring and controlling of material and information flows among different organizations. The trend has been toward even more flexible or dynamic supply chains to find suitable business partners and enter into profitable contracts. Previous studies have proposed a two-layered supply chain model consisting of two kinds of organization: clients and suppliers. This study proposes a new model representing multi-layered dynamic supply chains and a negotiation protocol in multi-layered organizations. The organizations in the middle-layers generate both orders of parts for suppliers and offers of products for clients. Production schedules in the middle-layers continue to be modified after orders are sent to suppliers. Suppliers simultaneously generate and modify sets of production schedules for individual orders to find the most profitable order of all. The effectiveness of the model and the negotiation protocol is evaluated through computational experiments.
Cite this article as:
Y. Tanimizu, C. Ozawa, Y. Shimizu, B. Orita, K. Iwamura, and N. Sugimura, “Flexible Multi-Layered Dynamic Supply Chain Models with Cooperative Negotiation,” Int. J. Automation Technol., Vol.7 No.1, pp. 128-135, 2013.
Data files:
  1. [1] D. Emerson and S. Piramuthu, “Agent-based Framework for Dynamic Supply Chain Configuration,” Proc. of 37th Hawaii International Conference on System Science, 70168a, CD-ROM, 2004.
  2. [2] S. Piramuthu, “Knowledge-based Framework for Automated Dynamic Supply Chain Configuration,” European J. of Operational Research, Vol.165, pp. 219-230, 2005.
  3. [3] T. Kaihara and S. Fujii, “IT based Virtual Enterprise Coalition Strategy in Agile Manufacturing Environment,” Proc. of 35th CIRP Int. Seminar on Manufacturing Systems, pp. 333-338, 2002.
  4. [4] T. Nishi, M. Konishi, Y. Hattori, and S. Hasebe, “A Decentralized Supply Chain Optimization Method for Single Stage Production Systems,” Trans. of System Control Information, 47, 12, pp. 628-636, 2003.
  5. [5] Y. Tanimizu, M. Yamanaka et al., “Multi-agent Based Dynamic Supply Chain Configuration Considering Production Schedules,” Proc. of 2006 Int. Symp. on Flexible Automation, pp. 572-578, 2006.
  6. [6] Y. Tanimizu, C. Ozawa et al., “Credibility of Supplier in Dynamic Supply Chain,” Proc. of 40th CIRP Int. Seminar on Manufacturing Systems, CD-ROM, 2007.
  7. [7] J. H. Holland, “Adaptation in Natural and Artificial Systems,” Univ. of Michigan Press, Ann Arbor, MI, 1975.
  8. [8] Y. Tanimizu, T. Sakaguchi et al., “Evolutional Reactive Scheduling for Agile Manufacturing Systems,” Int. J. of Production Research, Vol.44, Nos.18-19, pp. 3727-3742, 2006.
  9. [9] S. Lawrence, “Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques (Supplement),” Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, Pennsylvania, 1984.

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