摘要
预售阶段产品扩散和口碑传播具有不确定性,难以分析口碑以及扩散对企业定价决策的影响,鉴于此构建了扩散效应下的产品定价模型和双口碑效应下的产品定价模型。同时,针对现有人工蜂群算法收敛速度慢且对于定价问题的求解匹配度较低等问题,提出了一种从众人工蜂群算法求解上述模型。该算法在邻域搜索解时,根据安全位置展开安全邻域搜索,提高搜索能力和多样性。实验表明:改进后的人工蜂群算法在寻优过程迭代更快,可求解得到企业在预售阶段和销售阶段的最优定价策略,保障企业在大型活动中获得最大利润。
Product diffusion and word-of-mouth communication in the pre-sale stage are uncertain,and it is difficult to analyze the influence of word-of-mouth and diffusion on enterprise pricing decisions.Therefore,a product pricing model under the diffusion effect and a product pricing model under the dual word-of-mouth effect are constructed.At the same time,to solve the problems of slow convergence speed of the existing artificial bee colony algorithm and low matching degree for solving the pricing problem,a crowd artificial bee colony algorithm is proposed.When searching for solutions in the neighborhood,the algorithm conducts a safe neighborhood search according to the safe location to improve the search capability and diversity.Experiments show that the improved artificial bee colony algorithm iterates faster in the optimization process,and obtains the optimal pricing strategy of the enterprise in the pre-sale and sales stages,ensuring that the enterprise can obtain the maximum profit in large-scale activities.
作者
朱珠
张璐
ZHU Zhu;ZHANG Lu(Institute of Information,Liaoning University,Shenyang 110036,China)
出处
《控制工程》
CSCD
北大核心
2024年第8期1373-1382,共10页
Control Engineering of China
基金
国家自然科学基金项目(72102096)
教育部人文社会科学研究项目(18YJC630276)。
关键词
产品定价
产品扩散
口碑
人工蜂群算法
从众行为
Product pricing
product proliferation
word of mouth
artificial bee colony algorithm
herd behavior
