计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于动态选择性集成学习的供应链产销协商优化策略

武玉英,严勇,蒋国瑞   

  1. (北京工业大学 经济与管理学院,北京 100122)
  • 收稿日期:2016-07-28 出版日期:2017-05-15 发布日期:2017-05-15
  • 作者简介:武玉英(1966—),女,副教授、博士,主研方向为商务智能、系统工程;严勇,硕士研究生;蒋国瑞,教授、博士。
  • 基金项目:
    国家自然科学基金(71371018)。

Negotiation Optimization Strategy of Supply Chain Production and Marketing Based on Dynamic Selective Ensemble Learning

WU Yuying,YAN Yong,JIANG Guorui   

  1. (College of Economics and Management,Beijing University of Technology,Beijing 100122,China)
  • Received:2016-07-28 Online:2017-05-15 Published:2017-05-15

摘要: 针对当前Agent产销协商自适应学习效果差及协商环境动态变化的现状,考虑动态协商环境中的冲突水平、合作可能性、协商剩余时间对谈判的影响,利用熵值法确定3个影响因素的权重并进行线性加权。结合当前协商议题的差异性,构建基于动态选择性集成学习的让步幅度预测模型,并提出供应链产销协商优化策略。实验结果表明,与单学习机协商策略相比,该策略提高了Agent自适应学习成功率及联合效用,并且能确保供应链产销双方受益,实现合作双方互利互赢的局面。

关键词: 动态选择性集成学习, 动态协商环境, Agent产销协商, 自适应学习, 熵值法

Abstract: Aiming at the situations of poor adaptive learning effect and dynamically changed negotiation environment of current Agent production and marketing negotiation,the influence of conflict level,cooperation possibility and negotiation remaining time on negotiation in the dynamic negotiation environment are considered,and the entropy evaluation method is used to determine the weights of the three factors and perform linear weighting.According to the difference of the current negotiation issues,this paper constructs a model for predicting the concession amplitude based on dynamic selective ensemble learning,and proposes a negotiation optimization strategy of supply chain production and marketing.The experimental results show that compared with the single machine learning negotiation strategy,the proposed strategy improves the adaptive learning success rate and joint utility of Agent.It can ensure mutual benefit of both sides of the supply chain production and marketing to achieve win-win situation of cooperation between the two sides.

Key words: dynamic selective ensemble learning, dynamic negotiation environment, Agent production and marketing negation, self-adaptive learning, entropy evaluation method

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