Conflict Resolution of Production-marketing Collaborative Planning based on Multi-Agent Self-adaptation Negotiation

Hao Li, Ting Pang, Yuying Wu, Guorui Jiang

2014

Abstract

In order to overcome the lack of adaptability and learning ability of traditional negotiation, we regard supply chain production-marketing collaborative planning negotiation as the research object, design one five-elements negotiation model, adopt a negotiation strategy based on Q-reinforcement learning, and optimize the negotiation strategy by the RBF neural network and predict the information of opponent for adjusting the concession extent. At last, we give a sample that verifies the negotiation strategy can enhance the ability of the negotiation Agents, reduce the negotiation times, and improve the efficiency of resolving the conflicts of production-marketing collaborative planning, comparing to the un-optimized Q-reinforcement learning.

References

  1. Hao, J. Y., Cheng H. F., 2012. An Adaptive Bilateral Negotiating Strategy over Multiple Items. In Proceedings of IEEE International Conferences on Web Intelligence and Intelligent Agent Technology.
  2. Wang, G., Wong, T. N., Yu, C.X., 2013. A Computational Model for Multi-agent E-commerce Negotiations with Adaptive Negotiation Behaviors. In Journal of Computational Science.
  3. Kumar V., Mishra N., 2011. A Multi-agent Self Correcting Architecture for Distributed Manufacturing Supply Chain. In IEEE Systems Journal.
  4. Sara S., Ali S., Reza S., 2012. Applying Agent-Based System and Negotiation Mechanism in Improvement of Inventory Management and Customer Order Fulfillment in Multi Echelon Supply Chain. In Arabian Journal for Science and Engineering.
  5. Yu C., Gao J., Gu M. H., etc., 2009. Automatic Negotiation Decision Model Based on Machine Learning. In Journal of Software.
  6. Watkins C., Dayan P., 1992. Q-reinforcement learning. In Machine Learning.
  7. Sui X., Cai G.Y., Shi L, 2010. Multi-agent Negotiation Strategy and Algorithm Based on Q-Learning. In Computer Engineering.
  8. Chun S., Lei L., Fan L., etc, 2012. An Adaptive Market-driven Agent Based on Multi-agent Reinforcement Learning for Automated Negotiation. In International Journal of Digital Content Technology and its Applications.
  9. Ariel M., AnalĂ­a A., 2013. A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation. In Expert Systems with Applications.
  10. Shen, J., 2007. Hierarchical Reinforcement Learning Theory and Method. In Harbin Engineering University Press.
  11. Shi, Z. Z., 2009. Neural Network. In Higher Education Press, Beijing.
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Paper Citation


in Harvard Style

Li H., Pang T., Wu Y. and Jiang G. (2014). Conflict Resolution of Production-marketing Collaborative Planning based on Multi-Agent Self-adaptation Negotiation . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-016-1, pages 209-214. DOI: 10.5220/0004830602090214


in Bibtex Style

@conference{icaart14,
author={Hao Li and Ting Pang and Yuying Wu and Guorui Jiang},
title={Conflict Resolution of Production-marketing Collaborative Planning based on Multi-Agent Self-adaptation Negotiation},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2014},
pages={209-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004830602090214},
isbn={978-989-758-016-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Conflict Resolution of Production-marketing Collaborative Planning based on Multi-Agent Self-adaptation Negotiation
SN - 978-989-758-016-1
AU - Li H.
AU - Pang T.
AU - Wu Y.
AU - Jiang G.
PY - 2014
SP - 209
EP - 214
DO - 10.5220/0004830602090214