Personalized customization: Service resource configuration optimization driven by customer requirements accurately - Chao Yu; Haibin Wang (PLoS ONE, 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).