计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 245-255.doi: 10.11896/jsjkx.200800173
姚娟1, 邢镔2, 曾骏1, 文俊浩1
YAO Juan1, XING Bin2, ZENG Jun1, WEN Jun-hao1
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。
中图分类号:
[1]LI B H,ZHANG L,CHAI X D.Introduction to cloud manufacturing[J].ZTE Communications,2010,16(4):6-8. [2]LI B H,ZHAO X P,ZHANG L, et al.Further discussion oncloud manufacturing[J].Computer Integrated Manufacturing Systems,2011,17(3):449-457. [3]TAO F,ZHANG L,GUO H,et al.Typical characteristics ofcloud manufacturing and several kay issues of cloud service composition [J].Computer Integrated Manufacturing Systems,2011,17(3):477-486. [4]ROSING M V,WHITE S,CUMMINS F,et al.Business Process Model and Notation[M].Springer Berlin Heidelberg,2011. [5]BENATALLAH B,SHENG Q Z,DUMAS M.The Self-Servenvironment for Web services composition[J].IEEE Internet Computing,2003,7(1):40-48. [6]WANG J,CHANG L,ZHU C,et al.Reasoning about Semantic Web Services with an Approach Based on Temporal Description Logic[C]//Intelligent Information Processing VI-7th IFIP TC 12 International Conference(IIP 2012).Berlin,German:Springer,2012:286-294. [7]AKKIRAJU R,SRIVASTAVA B,IVAN A A,et al.SEMAPLAN:Combining planning with semantic matching to achieve Web service compositi-on[C]//ICWS 2006:IEEE International Conference on Web Services,proceedings.Los Alamitos:IEEE Computer Soc,2006:37. [8]ZHAO H B,DOSHI P.A hierarchical framework for logicalcomposition of web services[J].SOCA,2009,3(4):285-306. [9]SILVA A S D,MA H,ZHANG M.GraphEvol:A Graph Evolution Technique for Web Service Composition[C]//Database and Expert Systems Applications-26th International Conference(DEXA 2015).Berlin,German:Springer Verlag,2015:134-142. [10]HASHEMIAN S V,MAVADDAT F.A Graph-Based Framework for Composition of Stateless Web Services[C]//Procee-dings of ECOWS 2006:Fourth European Conference on Web Services.Computer Society,2006:75-86. [11]HAYYOLALAM V,KAZEM A A P.A systematic literaturereview on QoS-aware service composition and selection in cloud environment[J].Journal of Network and Computer Applications,2018,110:52-74. [12]SHE Q P,WEI X C,NIE G H,et al.QoS-aware cloud service composition:A systematic mapping study from the perspective of computational intelligence[J].Expert Systems with Applications,2019,138:112804. [13]GABREL V,MANOUVRIER M,MOREAU K,et al.QoS-aware automatic syntactic service composition problem:Comple-xity and resolution[J].Future Generation Computer Systems-the International Journal of Escience,2018,80:311-321. [14]LI W J,DING Y,YANG Y J,et al.Parameterized algorithms of fundamental NP-hard problems:a survey[J].Human-Centric Computing and Information Sciences,2020,10(1). [15]HILLAR C J,LIM L H.Most Tensor Problems Are NP-Hard[J].Journal of the Acm,2013,60(6). [16]ZHANG W Y,YANG Y S,ZHANG S,et al.Correlation-aware manufacturing service composition model using an extended flower pollination algorithm[J].International Journal of Production Research,2018,56(14):4676-4691. [17]LI Y X,YAO X F,LIU M.Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm[J].Mathematical Problems in Engineering,2019,2019:1-19. [18]NASERI A,NAVIMIPOUR N J.A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm[J].Journal of Ambient Intelligence and Humanized Computing,2019,10(5):1851-1864. [19]LIN T Y,LI B H,CHAI X D,et al.Cloud manufacturing oriented automatic composition tech-nology of models[J].Computer Integrated Manufacturing Systems,2012,18(7):1379-1386. [20]LARTIGAU J,XU X,NIE L,et al.Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm[J].International Journal of Production Research,2015,53(14):4380-4404. [21]LI F,ZHANG L,LIU Y K,et al.A clustering network-basedapproach to service composition in cloud manufacturing[J].International Journal of Computer Integrated Manufacturing,2017,30(12):1331-1342. [22]ZHOU J J,YAO X F,LIN Y Z,et al.An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing[J].Information Sciences,2018,456:50-82. [23]LU Y Q,XU X.A semantic web-based framework for service composition in a cloud manufa-cturing environment[J].Journal of Manufacturing Systems,2017,42:69-81. [24]GAVVALA S K,JATOTH C,GANGADHARAN G R,et al.QoS-aware cloud service composition using eagle strategy[J].Future Generation Computer Systems,2019,90:273-290. [25]YUAN M H,ZHOU Z,CAI X X,et al.Service compositionmodel and method in cloud manufacturing[J].Robotics and Computer-Integrated Manufacturing,2020,61:101840.1-101840.13. [26]ZHANG Y K.Research on Combination Optimi-zation of Cloud Manufacturing Service Based on Ant Colony Algorithm[D].Nanjing:Nanjing University of Posts and Telecommunications,2018. [27]MA W L,WANG Z,ZHAO Y W.Optimizing services composition in cloud manufacturing based on improved ant colony algorithm[J].Computer Integrated Manufacturing Systems,2016,22(1):113-121. [28]ZHOU J J,YAO X F.Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing[J].Applied Soft Computing,2017,56:379-397. [29]YANG Y F,YANG B,WANG S L,et al.An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing[J/OL].Applied Soft Computing.http://doi.org/10.1016/j.asoc.2019.106003.2. [30]YANG Y F,YANG B,WANG S L,et al.An Improved GreyWolf Optimizer Algorithm for Energy-Aware Service Composition in Cloud Manufacturing[J].The International Journal of Advanced Manufacturing Technology,2019,105:3079-3091. [31]JIN H,YAO X F,CHEN Y.Correlation-aware QoS modeling and manufacturing cloud service composition[J].Journal of Intelligent Manufacturing,2015,28(8):1947-1960. [32]ZHOU J J,YAO X F.A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition[J].The International Journal of Advanced Manufacturing Technology,2016,88(9/10/11/12):3371-3387. [33]LIU Z Z,SONG C,CHU D H,et al.An Approach for Multipath Cloud Manufacturing Services Dynamic Composition[J].International Journal of Intelligent Systems,2017,32(4):371-393. [34]WU Y X,JIA G Z,CHENG Y.Cloud manufacturing servicecomposition and optimal selection with sustainability considerations:a multi-objective integer bilevel multi-follower programming approach[J].International Journal of Production Research,2019,58(19):6024-6042. [35]BOUZARY H,CHEN F F.A classification-based approach for integrated service matching and composition in cloud manufacturing[J/OL].Robotics and Computer-Integrated Manufactu-ring.http://doi.org/10.1016/j.rcim.2020.101989. [36]LIU B,ZHANG Z L.QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups[J].The International Journal of Advanced Manufactu-ring Technology,2016,88(9/10/11/12):2757-2771. [37]ZHANG Y F,XI D,LI R,et al.Task-driven manufacturingcloud service proactive discovery and optimal configuration method[J].The International Journal of Advanced Manufactu-ring Technology,2015,84(1/2/3/4):29-45. [38]XIANG F,JIANG G Z,XU L L,et al.The case-library method for service composition and optimal selection of big manufactu-ring data in cloud manufacturing system[J].The International Journal of Advanced Manufacturing Technology,2015,84(1/2/3/4):59-70. [39]HUANG B Q,LI C H,TAO F.A chaos control optimal algorithm for QoS-based service composition selection in cloud manu-facturing system[J].Enterprise Information Systems,2014,8(4):445-463. [40]LIU Y K,XU X,ZHANG L,et al.An Extensible Model for Multitask-Oriented Service Composition and Scheduling in Cloud Manufacturing[J].Journal of Computing and Information Science in Engineering,2016,16(4):041009. [41]LI H B,CHAN K,LIANG M X,et al.Composition of Resource-Service Chain for Cloud Manufacturing[J].IEEE Transactions on Industrial Informatics,2015,12(1):211-219. [42]CAO Y L,WU Z J,LIU T,et al.Multivariate process capability evaluation of cloud manufacturing resource based on intuitionistic fuzzy set[J].The International Journal of Advanced Manufacturing Technology,2015,84(1/2/3/4):227-237. [43]YI A B,YAO X F,ZHOU H F,et al.Multi-objective optimal selection of equipment resources in cloud manufacturing[J].Computer Integrated Manufacturing Systems,2017,23(6):1187-1195. [44]SEGHIR F,KHABABA A.A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition[J].Journal of Intelligent Manufacturing,2016,29(8):1773-1792. [45]JIN H,YAO X F,YANG Z,et al.Manufacturing cloud service composition of teaching-learning based optimization[J].Computer Integrated Manufacturing Systems,2018,24(1):43-52. [46]ARUNARANI A R,MANJULA D,SUGUMARAN V.Taskscheduling techniques in cloud computing:A literature survey[J].Future Generation Computer Systems,2019,91:407-415. [47]QUE Y,ZHONG W,CHEN H L,et al.Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing[J].International Journal of Advanced Manufacturing Technology,2018,96(9/10/11/12):4455-4465. [48]BOUZARY H,CHEN F F.A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing[J].International Journal of Advanced Manufacturing Technology,2019,101(9/10/11/12):2771-2784. [49]FAZELI M M,FARJAMI Y,NICKRAY M.An ensemble optimisation approach to service composition in cloud manufactu-ring[J].International Journal of Computer Integrated Manufacturing,2018,32(1):83-91. [50]ZHOU J J,YAO X F.A hybrid approach combining modifiedartificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition[J].Internatio-nal Journal of Production Research,2017,55(16):4765-4784. [51]ZHOU J J,YAO X F.Hybrid teaching-learning-based optimization of correlation-aware service composition in cloud manufacturing[J].The International Journal of Advanced Manufacturing Technology,2017,91(9/10/11/12):3515-3533. [52]YANG C,SHEN W M,LIN T Y,et al.IoT-enabled dynamic service selection across multiple manufacturing clouds[J].Manu-facturing Letters,2016,7:22-25. [53]LI F,ZHANG L,LIU Y K,et al.QoS-Aware Service Composition in Cloud Manufacturing:A Gale-Shapley Algorithm-Based Approach[J].IEEE Transactions on Systems Man Cybernetics-Systems,2020,50(7):2386-2397. [54]WANG F,LAILI Y J,ZHANG L.A many-objective memeticalgorithm for correlation-aware service composition in cloud manufacturing[J].International Journal of Production Research,2020:1-19. [55]ZHANG Y F,ZHANG G,QU T,et al.Analytical target cascading for optimal configuration of cloud manufacturing services[J].Journal of Cleaner Production,2017,151:330-343. [56]AKBARIPOUR H,HOUSHMAND M.Service composition and optimal selection in cloud manufacturing:landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm[J].Neural Computing & Applications,2020,32(15):10873-10894. [57]BOUZARY H,CHEN F F.Service optimal selection and composition in cloud manufacturing:a comprehensive survey[J].The International Journal of Advanced Manufacturing Technology,2018,97(1/2/3/4):795-808. [58]TAO F,ZHANG L,LIU Y K,et al.Manufacturing ServiceManagement in Cloud Manufacturing:Overview and Future Research Directions[J].Journal of Manufacturing Science and Engineering-Transactions of the Asme,2015,137(4). |
[1] | 陈俊, 何庆, 李守玉. 基于自适应反馈调节因子的阿基米德优化算法 Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor 计算机科学, 2022, 49(8): 237-246. https://doi.org/10.11896/jsjkx.210700150 |
[2] | 刘漳辉, 郑鸿强, 张建山, 陈哲毅. 多无人机使能移动边缘计算系统中的计算卸载与部署优化 Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems 计算机科学, 2022, 49(6A): 619-627. https://doi.org/10.11896/jsjkx.210600165 |
[3] | 黄国兴, 杨泽铭, 卢为党, 彭宏, 王静文. 利用粒子滤波方法求解数据包络分析问题 Solve Data Envelopment Analysis Problems with Particle Filter 计算机科学, 2022, 49(6A): 159-164. https://doi.org/10.11896/jsjkx.210600110 |
[4] | 储安琪, 丁志军. 基于灰狼优化算法的信用评估样本均衡化与特征选择同步处理 Application of Gray Wolf Optimization Algorithm on Synchronous Processing of Sample Equalization and Feature Selection in Credit Evaluation 计算机科学, 2022, 49(4): 134-139. https://doi.org/10.11896/jsjkx.210300075 |
[5] | 杨玉丽, 李宇航, 邓岸华. 面向个性化需求的云制造服务可信评价模型 Trust Evaluation Model of Cloud Manufacturing Services for Personalized Needs 计算机科学, 2022, 49(3): 354-359. https://doi.org/10.11896/jsjkx.210200116 |
[6] | 屈立成, 吕娇, 屈艺华, 王海飞. 基于模糊神经网络的运动目标智能分配定位算法 Intelligent Assignment and Positioning Algorithm of Moving Target Based on Fuzzy Neural Network 计算机科学, 2021, 48(8): 246-252. https://doi.org/10.11896/jsjkx.200600050 |
[7] | 孙明玮, 司维超, 董琪. 基于多维度数据的网络服务质量的综合评估研究 Research on Comprehensive Evaluation of Network Quality of Service Based on Multidimensional Data 计算机科学, 2021, 48(6A): 246-249. https://doi.org/10.11896/jsjkx.200900131 |
[8] | 章菊, 李学鋆. 基于莱维萤火虫算法的智能生产线调度问题研究 Research on Intelligent Production Line Scheduling Problem Based on LGSO Algorithm 计算机科学, 2021, 48(6A): 668-672. https://doi.org/10.11896/jsjkx.210300118 |
[9] | 杨林, 王永杰. 蚁群算法在动态网络持续性路径预测中的运用及仿真 Application and Simulation of Ant Colony Algorithm in Continuous Path Prediction of Dynamic Network 计算机科学, 2021, 48(6A): 485-490. https://doi.org/10.11896/jsjkx.200800132 |
[10] | 张蔷, 黄樟灿, 谈庆, 李华峰, 湛航. 基于动态近邻套索算子的金字塔演化策略 Pyramid Evolution Strategy Based on Dynamic Neighbor Lasso 计算机科学, 2021, 48(6): 215-221. https://doi.org/10.11896/jsjkx.200400115 |
[11] | 郑增乾, 王锟, 赵涛, 蒋维, 孟利民. 带宽和时延受限的流媒体服务器集群负载均衡机制 Load Balancing Mechanism for Bandwidth and Time-delay Constrained Streaming Media Server Cluster 计算机科学, 2021, 48(6): 261-267. https://doi.org/10.11896/jsjkx.200400131 |
[12] | 陆懿帆, 曹芮浩, 王俊丽, 闫春钢. 一种基于微服务的检察业务服务封装方法 Method of Encapsulating Procuratorate Affair Services Based on Microservices 计算机科学, 2021, 48(2): 33-40. https://doi.org/10.11896/jsjkx.191100152 |
[13] | 刘奇, 陈红梅, 罗川. 基于改进的蝗虫优化算法的红细胞供应预测方法 Method for Prediction of Red Blood Cells Supply Based on Improved Grasshopper Optimization Algorithm 计算机科学, 2021, 48(2): 224-230. https://doi.org/10.11896/jsjkx.200600016 |
[14] | 刘华玲, 皮常鹏, 刘梦瑶, 汤新. 一种新的优化机制:Rain New Optimization Mechanism:Rain 计算机科学, 2021, 48(11A): 63-70. https://doi.org/10.11896/jsjkx.201100032 |
[15] | 魏昕, 冯锋. 基于高斯-柯西变异的帝国竞争算法优化 Optimization of Empire Competition Algorithm Based on Gauss-Cauchy Mutation 计算机科学, 2021, 48(11A): 142-146. https://doi.org/10.11896/jsjkx.201200071 |
|