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。
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.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第5期191-196,共6页
Computer Engineering
基金
国家自然科学基金(71371018)
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
dynamic selective ensemble learning
dynamic negotiation environment
Agent production and marketing negation
self-adaptive learning
entropy evaluation method
