Yu Chao, Wang Haibin
School of Management, Shenyang University of Technology, Shenyang, China.
PLoS One. 2025 Apr 14;20(4):e0320312. doi: 10.1371/journal.pone.0320312. eCollection 2025.
Proposing an approach of service resource configuration optimization driven by customer requirements to address the issue of service resource configuration optimization in the context of personalized customization. Firstly, the importance judgment matrix, KANO model, and competitiveness evaluation are integrated to evaluate the relative importance of customer requirements. Secondly, the House of Quality (HoQ) and the intermediary variable "technical attributes" are utilized to determine the weight of each service module and its correlation with customer requirements. Afterwards, due to the varying customer requirements, the service candidate itemsets under the same service module will differ. To address this, a "one-to-many" relationship mechanism is introduced between the service module and service candidate itemsets. The service candidate itemsets are determined based on the correlated customer requirements. On this basis, the customer's perceived utility is determined by applying the four types of utility measure functions. The service resource configuration scheme is established by formulating and solving an optimization model. Finally, the viability and efficacy of the approach are demonstrated with an example of living room customization by a customization company, utilizing an improved genetic algorithm (IGA).
提出一种由客户需求驱动的服务资源配置优化方法，以解决个性化定制背景下的服务资源配置优化问题。首先，综合重要性判断矩阵、KANO模型和竞争力评估来评估客户需求的相对重要性。其次，利用质量屋（HoQ）和中间变量“技术属性”来确定每个服务模块的权重及其与客户需求的相关性。之后，由于客户需求各异，同一服务模块下的服务候选项目集也会不同。为此，在服务模块与服务候选项目集之间引入“一对多”关系机制。服务候选项目集根据相关客户需求确定。在此基础上，通过应用四种效用度量函数来确定客户的感知效用。通过制定和求解优化模型来建立服务资源配置方案。最后，以一家定制公司的客厅定制为例，利用改进的遗传算法（IGA）证明该方法的可行性和有效性。
