Manufacturing service recommendation method toward industrial internet platform considering the cooperative relationship among enterprises. (15th April 2022)

• Record Type: Journal Article
• Authors: Wang, Lei; Gao, Tianyi; Zhou, Bin; Tang, Hongtao; Xiang, Feng
• Is Part Of: Expert systems with applications. Volume 192(2022)
• Publication Date: 2022-04-15
• Subjects: Manufacturing service collaboration -- Multi-attribute recommendation -- Collaboration relationship -- Data mining; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116391
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The collaboration between services were considered in recommendation. An evaluation indicator named manufacturing service collaboration frequency indicator (CFI) were defined. The CFI of service combinations were calculated by FP-growth and Simrank. Optimizing service combinations with a MOEA combining CFI filtering (MOEA + CFIF). MOEA + CFIF obtained solutions with both high CFI and QoS. Abstract: The popularization of the service-oriented manufacturing mode makes increasing customers configure the required manufacturing services from Industry Internet platforms. However, most recommendation methods mainly focus on the QoS indicators of the overall manufacturing service combination without considering the collaboration relationship between manufacturing services. A novel multi-attribute recommendation method of manufacturing service combination considering the historical collaboration relationship of the online platforms is studied in this paper. First, an evaluation indicator named manufacturing service collaboration frequency indicator (CFI) is introduced, which uses an improved machine learning algorithm combining FP-growth and Simrank to mine the frequent terms and similarities of the manufacturing service collaboration process. Then, a multi-objective evolutionary algorithm considering CFI and QoS is proposed to recommend a more reliable combination of manufacturing services for customers. Finally, comparative experiments are conducted to demonstrate the effectiveness and practicability of our method.