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Conflict Resolution of Production-Marketing Collaborative Planning
based on Multi-Agent Self-adaptation Negotiation
Hao Li, Ting Pang, Yuying Wu and Guorui Jiang
The Economics and Management school, Beijing University of Technology,
No. 100, Pingleyuan, Chaoyang District, Beijing, China
Keywords: Production-Marketing Collaborative Conflict, Multi-Agent, Self-adaptation Negotiation, RBF Neural
Network, Q-reinforcement Learning.
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.
1 INTRODUCTION
In order to meet the market requirements of the
dynamic changes quickly and timely, the retailer and
manufacturer on the supply chain establish contract
early and draw up merchandise procurement plan.
Now, the production-marketing planning changes
from the simple trading to the consideration of
general interests of supply chain. However, because
the objectives of different enterprise are always
various, their disagreements and conflicts appear
usually.
Distributed Agent technology have characters
such as interaction, autonomy and learning (Wang,
2013), it is not restricted by time and space, using
the negotiation means based on multi-agent in the
supply chain production-marketing collaborative
planning, not only resolves conflicts but also solves
the enterprise-decentralization problem. Many
scholars have used the Agent technology in the
negotiation of supply chain. For example,Kumar
proposed a multi-agent system, selected the best
supplier by automatic negotiation based on cost,
distance and quality(Kumar, 2011); Sara used the
multi-agent system to simulate multi-layer supply
chain, controlled the inventory and cost by sharing
information, predicting knowledge (Sara, 2012). In
order to adapt to environment and the opponent’s
dynamic information, the scholars in the intelligent
negotiation field began to introduce self-learning
mechanism into negotiation. For instance, Cheng
learned the opponent’s utility function by using
SVM (Cheng, 2009). Q-reinforcement learning is an
algorithm which is independent of environment
model, proposed by Watkins(Watkins, 1992), each
action of Agent during negotiating has a return
function value Q, then evaluates the present action of
Agent and predicts the next action of Agent,
accomplishes the proposed process by calculating
Q(Sui, 2010). Many studies have introduced
Q-reinforcement learning into collaborative
negotiations (Shen, 2012; Ariel, 2013) to resolve
conflicts effectively, optimize collaborative effect.
The studies above all improve the running
effectiveness of supply chain, but still there are some
shortages. Now there are fewer studies on supply
chain negotiation adopting adaptive algorithms;
self-learning ability and adaptability of negotiation
Agents are relative poor; most of negotiation
strategies based on Q-reinforcement learning are not
self-adaptive, there are lesser studies on adjusting Q
value depending on opponents’ behavior, and the
convergence speed is still slow. To solve the
problems, we propose a multi-agent adaptive
negotiation method for the problem of supply chain
production-marketing collaborative planning conflict.
209
Li H., Pang T., Wu Y. and Jiang G..
Conflict Resolution of Production-marketing Collaborative Planning based on Multi-Agent Self-adaptation Negotiation.
DOI: 10.5220/0004830602090214
In Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART-2014), pages 209-214
ISBN: 978-989-758-016-1
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)