计算机工程与应用 ›› 2017, Vol. 53 ›› Issue (13): 21-28.DOI: 10.3778/j.issn.1002-8331.1702-0070

• 热点与综述 • 上一篇    下一篇

客户群及个体服务选择影响因子研究

李  冰1,2,王  虎3,王  锐4   

  1. 1.江西师范大学 软件学院,南昌 330022
    2.江西师范大学 管理决策评价研究中心,南昌 330022
    3.武汉理工大学,武汉 430070
    4.江西理工大学,江西 赣州 341000
  • 出版日期:2017-07-01 发布日期:2017-07-12

Research on influence factors of customer group and individual service selection

LI Bing1,2, WANG Hu3, WANG Rui4   

  1. 1.Software College, Jiangxi Normal University, Nanchang 330022, China
    2.Research Center of Management Decision Evaluation, Jiangxi Normal University, Nanchang 330022, China
    3.Wuhan University of Technology, Wuhan 430070, China
    4.Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
  • Online:2017-07-01 Published:2017-07-12

摘要: 客户在选择服务过程中,一方面会受到自身不断积累的经验的影响,另一方面也会受到群体当中其他成员意见的影响。通过研究客户个体以及客户之间的相互协作关系,得出客户群及个体服务选择的三个主要影响因子:惯性影响因子、个体影响因子和群体影响因子。通过粒子群优化算法的群体智能仿真工具,对客户服务选择的影响因子与选择结果之间的复杂非线性关系进行模拟仿真实验,通过定量数据的模拟,精确找出各个影响因子对选择结果的影响程度,选择结果对影响因子的敏感度,以及各个影响因子之间的最佳搭配比例。该研究对于优化客户与供应商服务之间双向选择提供了指导方向。

关键词: 群体智能, 粒子群优化算法, 客户群, 客户个体, 影响因子

Abstract: In the process service selection, customers will be the impact of their own continuous accumulation of experience, as well as the other member’sviews. Through analyzing the relationship of individual customer and their mutual cooperation relationship, this paper obtains three main factors of customer groups and individual service selection: inertial impact factors, individual factors and group factors. Using the particle swarm optimization algorithm, it simulates the complex nonlinear relationship between the influence factor on the selection of customer service and selection results. Through quantitative simulation data, it precisely identifies the influence degree of each influence factor, the selection results’ sensitivity on the factors, as well as optimal mix proportion between each influence factor. The research provides a direction for the optimization of the two-way selection between customers and suppliers.

Key words: swarm intelligence, Particle Swarm Optimization(PSO), customer group, individual customer, influencing factors