工业互联网环境下基于协作度的制造服务组合推荐方法*

doi:10.16731/j.cnki.1671-3133.2024.06.009

现代制造工程 ›› 2024, Vol. 525 ›› Issue (6): 69-74.doi: 10.16731/j.cnki.1671-3133.2024.06.009

工业互联网环境下基于协作度的制造服务组合推荐方法^*

袁东维¹, 凤飞龙²

1 西北政法大学管理学院,西安 710122;
2 陕西师范大学物理学与信息技术学院,西安 710119

收稿日期:2023-08-02 出版日期:2024-06-18 发布日期:2024-07-02
通讯作者: 袁东维,硕士,工程师,主要研究方向为服务计算及计算机工业控制等,曾发表论文十余篇。E-mail:dongzi76@163.com
作者简介:凤飞龙,博士,教授,主要研究方向为超声检测、计算机工业控制等。
基金资助:
*国家自然科学基金项目(11774212);陕西自然科学基金项目(2022JM-403);西北政法大学军民融合研究院项目(JM-202111)

Manufacturing service composition recommendation method based on collaboration under industrial internet environment

YUAN Dongwei¹, FENG Feilong²

1 School of Business, Northwest University of Political and Law, Xi’an 710122, China;
2 School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710119, China

Received:2023-08-02 Online:2024-06-18 Published:2024-07-02

摘要/Abstract

摘要： 在工业互联网环境下,一个制造任务通常由多个制造服务组成,每个服务的完成都不是独立的,它们之间协作的有效性是服务组合成功执行的关键因素之一。首先根据工业互联网中服务历史调用次数定义了协作度;然后基于协作度改进了服务成本和时间的衡量方法;最后提出了基于协作度的制造服务组合推荐方法,利用非支配遗传算法(Non-dominated Sorting Genetic Algorithm Ⅱ,NSGA-Ⅱ)为用户推荐服务质量优化的制造服务组合方案。通过与3种常用启发式算法的对比实验,验证了该算法的实用性和有效性。

关键词: 工业互联网, 制造服务, 非支配遗传算法, 服务推荐

Abstract: Under industrial internet environment, a manufacturing task usually consists of multiple manufacturing services, each of which is not completed independently, and the effectiveness of their collaboration is one of the key factors for success of the service composition. Firstly, based on the number of historical service invocations in the industrial internet platform, the collaboration is defined; secondly, the service cost and time measures are improved based on the collaboration; finally, manufacturing service composition recommendation method based on collaboration is proposed and the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) is used to recommend the manufacturing service composition scheme with service quality optimization for users. The practicality and effectiveness of this algorithm are verified by comparison with three heuristic algorithms.

Key words: industrial internet, manufacturing service, Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ), service recommendation

中图分类号:

TP391

袁东维, 凤飞龙. 工业互联网环境下基于协作度的制造服务组合推荐方法^*[J]. 现代制造工程, 2024, 525(6): 69-74.

YUAN Dongwei, FENG Feilong. Manufacturing service composition recommendation method based on collaboration under industrial internet environment[J]. Modern Manufacturing Engineering, 2024, 525(6): 69-74.

参考文献

[1] SISINNI E, SAIFULLAH A, HAN S, et al. Industrial Internet of Things: Challenges, Opportunities, and Directions[J].IEEE Transactions on Industrial Informatics, 2018,14,(11):4724-4734.
[2] KANG J G, LIU Y, XIAO B,et al. Neural and Attentional Factorization Machine-Based Web API Ecommendation for Mashup Development[J]. IEEE Transactions on Network and Service Management, 2021,18(4):4183-4196.
[3] KANG J G, LIU Y, TANG M, et al. An Effective Dynamic Web Service Selection Strategy with Global Optimal QoS Based on Particle Swarm Optimization Algorithm[C]//Book an Effective Dynamic Web Service Selection Strategy with Global Optimal QoS Based on Particle Swarm Optimization Algorithm.[S.l.]:IEEE Computer Society, 2012:2274-2279.
[4] XIE N, TAN W, ZHENG X, et al. An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing[J]. Journal of Industrial Information Integration, 2021,23:100211.
[5] FEKIH H, MTIBAA S, BOUAMAMA S. An efficient user-centric web service composition based on harmony particle swarm optimization[J]. International Journal of Web Services Research, 2019,16(1):1-21.
[6] CHIFU V R, POP C B, SALOMIE I, et al. Hybrid honey bees mating optimization algorithm for identifying the near optimal solution in web service composition[J]. Computing and Informatics, 2017,36(5):1143-1172.
[7] 付超,肖明.云制造环境下的云服务组合优选方法[J].计算机应用研究,2014,31(6):1744-1747,1751.
[8] 胡强,田雨晴,綦浩泉,等.基于改进人工蜂群算法的云制造服务组合优化方法[J].通信学报,2023,44(1):200-210.
[9] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J].IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197.
[10] 杨从锐,钱谦,王锋,等.改进的自适应遗传算法在函数优化中的应用[J].计算机应用研究,2018,35(4):1042-1045.
[11] GADHVIB Bhargav,SAVSANI Vimal,PATEL Vivek.Multi-Objective Optimization of Vehicle Passive Suspension System Using NSGA-II,SPEA2 and PESA-II[J]. Procedia Technology,2016,23:361-368.
[12] PAN L, XU W, LI L, et al. Adaptive simulated binary crossover for rotated multi-objective optimization[J]. Swarm and Evoutionary Computation, 2021,60:100759.

工业互联网环境下基于协作度的制造服务组合推荐方法^*

Manufacturing service composition recommendation method based on collaboration under industrial internet environment

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