基于粗糙数BWM-TOPSIS的智能产品 服务系统评估研究  

Research on smart product-service systems evaluation based on rough number BWM-TOPSIS and its application

作　　者：温馨[1] 翟淑媛 WEN Xin;ZHAI Shuyuan(School of Management,Shenyang University of Technology,Shenyang,Liaoning 110870,China)

机构地区：[1]沈阳工业大学管理学院,辽宁沈阳110870

出　　处：《沈阳工业大学学报(社会科学版)》2025年第2期166-174,共9页Journal of Shenyang University of Technology(Social Sciences)

基　　金：教育部人文社会科学研究基金规划基金项目(22YJA630095)。

摘　　要：随着数字技术与产品深度融合趋势的显著发展,企业面临多元化市场需求以及消费者对个性化服务体验的高要求等问题。许多企业更加注重产品与服务的紧密结合,期望通过提升智能产品服务系统水平来满足消费群体多元化需求,增强其市场竞争力。当前关于智能产品服务系统的评估研究相对匮乏,企业难以准确认识自身解决方案的优势与不足,从而限制其优化与发展。基于现有文献和理论明确了智能产品服务系统的核心特征,并考虑设计智能产品服务系统的因素,构建包含用户体验、经济性、环境影响和服务4个方面10个二级指标的智能产品服务系统评价指标体系;采用粗糙数改进的BWM计算指标权重,准确描述模糊评价信息分布,以确保决策信息质量;同时利用粗糙数改进的TOPSIS法对备选方案进行排序,通过引入欧氏距离,结合粗糙集理论中的距离概念进行计算,体现数据的不确定特征;选取贴合研究场景的特斯拉Model 3、蔚来L7、小鹏P7i和蔚来ET5等4款新能源智能汽车产品作为备选方案进行实例计算,发现蔚来ET5的智能产品服务系统在本文评估标准下的运行表现较为突出。基于粗糙数改进的BWM和TOPSIS相结合的评价方法能够有效规避智能产品服务系统评价中的不确定性因素。粗糙数客观地整合群体意见,BWM和TOPSIS法相结合有效简化了计算过程,提高了判断的准确性,增加了权重结果的可靠性。选取符合应用场景的新能源汽车作为研究实例,充分证明了所构建评价模型的可行性和实效性,为智能产品服务系统的发展提供了有效的科学依据与参考,推动了智能产品服务系统评估领域的发展,也为企业实现商业模式革新、提升用户满意度和竞争力提供了理论支撑与实践指导。With the deep integration of digital technology and products,enterprises are facing problems such as diversified market demands and high demands from consumers for personalized service experiences.Many enterprises pay more attention to the close integration of products and services,hoping to meet the diversified needs of consumer groups and enhance their market competitiveness by improving the level of smart product-service systems.Currently,there is a relative lack of evaluation research on smart product-service systems,making it difficult for enterprises to accurately understand the advantages and disadvantages of their own solutions,thereby limiting their optimization and development.Based on existing literature and theory,this paper clarifies the core characteristics of smart product-service systems,considers factors related to the design of smart product-service systems,and constructs an evaluation index system for smart product-service systems,which includes 10 secondary indicators under four aspects:user experience,economy,environmental impact,and service.It uses the rough number improved best-worst method(BWM)to calculate indicator weights and accurately describes the distribution of fuzzy evaluation information to ensure the quality of decision information.At the same time,it uses the rough number improved TOPSIS method to rank the alternative solutions.By introducing Euclidean Distance and combining the concept of distance in rough set theory for calculation,the uncertain characteristics of the data are reflected.Four new energy intelligent vehicles,namely Tesla Model 3,NIO L7,Xiaopeng P7i,and NIO ET5,which are suitable for the research scenario,are selected as alternative solutions for example calculations.It is found that the smart product-service systems of NIO ET5 perform outstandingly under the evaluation criteria in this article.The evaluation method combining BWM and TOPSIS based on rough number improvement can effectively avoid uncertain factors in the evaluation of smart product-service systems

关 键 词：智能产品服务系统 新能源汽车 粗糙数 最优最劣方法 TOPSIS算法 

分 类 号：C934[经济管理—管理学]