全渠道消费者行为协同决策研究

发布时间：2018-03-26 05:43

本文选题：全渠道消费者行为　切入点：连锁零售供应链　出处：《计算机工程与科学》2017年08期

【摘要】：随着各大零售商全渠道营销战略布局,全渠道消费者数量呈爆炸式增长,对于全渠道消费者的消费行为研究成为热点。然而连锁零售供应链全渠道消费者消费数据呈现海量、高维的特征。针对这一特征,提出采用协同进化算法对连锁零售供应链全渠道消费者行为进行高维关联分析。利用粒子群优化算法和自适应遗传算法各自的优势,两个种群同时遍历,并在两种群间引入信息交互机制,使两种群协同进化。实证研究证明协同进化算法应用于连锁零售供应链全渠道消费大数据关联规则挖掘中,不仅算法的运算速度高,避免了遗传算法单独应用时容易陷入局部最优的缺陷,而且还提高了连锁零售供应链全渠道消费者行为关联规则的大数据挖掘质量,为全渠道消费者购买行为研究提供了新的方法。
[Abstract]:With the distribution of all channel marketing strategies of major retailers, the number of consumers in all channels is increasing explosively, and the research on consumer behavior of all channels has become a hot topic. However, the consumer consumption data of all channels in the chain retail supply chain are magnanimous. Aiming at the feature of high dimension, the co-evolutionary algorithm is proposed to analyze the consumer behavior of chain retail supply chain in high dimension. The advantages of particle swarm optimization algorithm and adaptive genetic algorithm are used to analyze the consumer behavior in chain retail supply chain. Two populations are traversed simultaneously, and information exchange mechanism is introduced between the two populations, which makes the two species co-evolve. The empirical study proves that the co-evolution algorithm is applied to the mining of big data association rules in the chain retail supply chain. Not only the speed of algorithm is high, but also the defect that genetic algorithm is easy to fall into local optimum when it is used alone, and the quality of big data mining the whole channel consumer behavior association rules of chain retail supply chain is improved. It provides a new method for the research of all-channel consumers' purchase behavior.
【作者单位】：北京工商大学计算机与信息工程学院;
【基金】：北京市自然科学基金(9162002) 教育部人文社会科学研究项目(09YJA630003) 首都流通业研究基地(JD-2014-YB-001) 2015年北京工商大学研究生科研能力提升计划
【分类号】：F713.55;TP18;TP311.13

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