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  • 标题:A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization
  • 本地全文:下载
  • 作者:Laila Esheiba ; Amal Elgammal ; Iman M. A. Helal
  • 期刊名称:Information
  • 电子版ISSN:2078-2489
  • 出版年度:2021
  • 卷号:12
  • 期号:8
  • 页码:296
  • DOI:10.3390/info12080296
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.
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