计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 245-255.doi: 10.11896/jsjkx.200800173

• 人工智能 • 上一篇    下一篇


姚娟1, 邢镔2, 曾骏1, 文俊浩1   

  1. 1 重庆大学大数据与软件学院 重庆401331
    2 重庆工业大数据创新中心有限公司 重庆400700
  • 收稿日期:2020-08-26 修回日期:2020-11-27 出版日期:2021-07-15 发布日期:2021-07-02
  • 通讯作者: 邢镔(xing.bin@hotmail.com)
  • 基金资助:

Survey on Cloud Manufacturing Service Composition

YAO Juan1, XING Bin2, ZENG Jun1, WEN Jun-hao1   

  1. 1 School of Big Data & Software Engineering,Chongqing University,Chongqing 401331,China
    2 Chongqing Innovation Center of Industrial Big-Data Co.Ltd,Chongqing 400700,China
  • Received:2020-08-26 Revised:2020-11-27 Online:2021-07-15 Published:2021-07-02
  • About author:YAO Juan,born in 1991,postgraduate,is a member of China Computer Federation.Her main research interests include cloud manufacturing service and service composition.(yaojuan@cqu.edu.cn)
    XING Bin,born in 1962,master,professor,senior engineer.His main research interests include application of indus-trial big-data technology and so on.
  • Supported by:
    National Key Research and Development Program of China(2019YFB1706104).

摘要: 随着工业化的飞速发展,制造业作为推动工业化的主力军必须加快发展步伐,因此,一种新的面向服务的制造模式——云制造被提出。云制造旨在在分布式制造资源和能力之间进行共享和协作并与需求构成一种按需的资源分配和使用方式,在选取最优性能服务的同时将这些服务组合成一个满足用户需求的复合服务需要不断进行探索。云制造服务组合是一种典型的NP-hard问题,是云制造最具有挑战性的课题之一。现阶段的云制造服务组合方法存在时间复杂度高、组合效果差、组合路径只能达到次优解等问题。如何利用微粒度的服务组合成复合服务以提升制造能力并满足用户需求已引起学术界和产业界研究人员的广泛关注,因此,对这种NP-hard问题的研究进行全面的综述是非常有必要的。文中首先对云制造服务组合中的组合流程和组合优化目标进行描述,然后从组合指标、优化算法和多目标与单目标优化问题等不同的角度对云制造服务组合中的重点和热点进行系统综述,最后对云制造服务组合的应用场景、实验数据和目前存在的不足进行概述和探讨。

关键词: 服务质量, 服务组合, 任务分解, 优化算法, 云制造

Abstract: With the rapid development of industrialization,manufacturing industry as the main force to promote industrialization must accelerate the pace of development,thus a new service-oriented manufacturing model——cloud manufacturing is proposed.Cloud manufacturing aims at sharing and cooperation between distributed manufacturing resources and capabilities,forms an on-demand resource allocation and uses mode with demand.It needs to explore continuously to select the optimal service performance and combine these services into a composite service to meet the needs of users.Cloud manufacturing service composition is an NP-hard problem,which is one of the most challenging problems in cloud manufacturing.The current cloud manufacturing service composition methods have challenges such as high time complexity,poor composition effect,and the composition path that can only achieve sub-optimal solutions.How to use fine-grained services to generate composite services to improve manufacturing capabilities and to meet users’ needs has attracted a widespread attention from academics and industrial researchers.Therefore,it is very necessary to conduct a comprehensive review of researches on this NP-hard problem.In this paper,firstly,the composition process and optimization objectives of cloud manufacturing service composition are described.Then,key points and hotspots in cloud manufacturing service composition are systematically summarized from different perspectives such as composition criteria,optimization algorithm,and multi-objective and single-objective optimization problems,etc.Finally,the application scenarios,experimental data and current deficiencies of cloud manufacturing service composition are summarized and discussed.

Key words: Cloud manufacturing, Optimization algorithm, Quality of Service(QoS), Service composition, Task decomposition


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