Abstract: To resolve the synergy conflict of supply chain production-marketing,this paper proposes a self-adaptive multi-Agent negotiation method.This method considers the equity concern behavior of Agents with the background of one manufacturer Agent negotiating with many retailer Agents.It adopts the inequity aversion model and uses the Radical Basis Function(RBF) neural network to optimize the Actor-Critic learning algorithm to predict and adjust the concession magnitude of Agents.The effectiveness and stability of the algorithm are verified through comparative experiments under different numbers of retailer Agents and different concern degree.The method can enhance the self-learning and adaptive ability of Agent,overcome the disadvantages of low learning efficiency and lack of equity concerns,shorten the negotiation time and improve the efficiency of resolving conflicts.

Key words: adaptive negotiation, supply chain collaboration, equity concerns, Actor-Critic reinforcement learning, Radical Basis Function(RBF) neural network

摘要： 为消解供应链产销协同计划冲突,提出一种多Agent自适应协商方法。在单个制造商Agent和多个销售商Agent协商情景下,考虑Agent的公平关切行为,采用不公平厌恶模型,通过径向基函数神经网络优化Actor-Critic学习算法,预测并调整双方Agent的让步幅度,提出自适应策略。在不同销售商Agent数量及关切程度下进行对比实验,结果表明,该方法增强了Agent的自学习和自适应能力,克服了学习效率慢且忽视公平关切行为的缺点,可实现缩短协商时间和提高冲突消解效率的目的。

关键词: 自适应协商, 供应链协同, 公平关切, Actor-Critic强化学习, 径向基函数神经网络

CLC Number: 

• TP18