Exploring the state-of-the-art service composition approaches in cloud manufacturing systems to enhance upcoming techniques

  • Vahideh Hayyolalam
  • Behrouz Pourghebleh
  • Ali Asghar Pourhaji KazemEmail author
  • Ali Ghaffari


Cloud manufacturing (CMfg) is a new paradigm that has been known as a promising integrated technology in which distributed resources of manufacturing are integrated and transformed into manufacturing services and handled centrally. It permits multiple users to request services simultaneously by submitting required tasks to a manufacturing platform. The service composition and optimal selection (SCOS) is the fundamental issue of CMfg that aims to select appropriate services from available manufacturing cloud resources to complete the manufacturing task and satisfy the users. Surveying and analyzing the available studies on this NP-hard problem is highly desirable. Therefore, as far as we know, this paper is the first research that tries to investigate and discuss the CMfg-SCOS methods in a systematic way. In this regard, the selected studies have been classified into two distinct groups comprising multi-objective and single-objective techniques. Furthermore, this research provides a comprehensive investigation of the current articles and compares them from several aspects based on various factors such as QoS parameters, other criteria, their adopted datasets, simulation tools, and algorithms. Moreover, hot papers, journals, and authors in this field have been revealed. In addition, the open challenging and the lacks within this issue have been discussed which can be applied to upcoming research.


Cloud manufacturing Service selection Systematic literature review Heuristic 



  1. 1.
    Li F, Zhang L, Liao TW, Liu Y (2018) Multi-objective optimisation of multi-task scheduling in cloud manufacturing. Int J Prod Res:1–17Google Scholar
  2. 2.
    Li B-H, Zhang L, Wang S-L, Tao F, Cao JW, Jiang XD, Song X, Chai XD (2010) Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manuf Syst 16(1):1–7Google Scholar
  3. 3.
    Zhou J, Yao X, Lin Y, Chan FTS, Li Y (2018) An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing. Inf Sci (Ny) 456:50–82MathSciNetCrossRefGoogle Scholar
  4. 4.
    Li F, Liao TW, Zhang L (2019) Two-level multi-task scheduling in a cloud manufacturing environment. Robot Comput Integr Manuf 56:127–139CrossRefGoogle Scholar
  5. 5.
    Pourghebleh B, Hayyolalam V (2019) A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things. Clust Comput:1–21Google Scholar
  6. 6.
    Pourghebleh B, Navimipour NJ (2017) Data aggregation mechanisms in the Internet of things: a systematic review of the literature and recommendations for future research. J Netw Comput ApplGoogle Scholar
  7. 7.
    Hayyolalam V, Pourhaji Kazem AA (2018) A systematic literature review on QoS-aware service composition and selection in cloud environment. J Netw Comput Appl 110:52–74CrossRefGoogle Scholar
  8. 8.
    Lin TY, Yang C, Zhuang C, Xiao Y, Tao F, Shi G, Geng C (2017) Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system. Proc Inst Mech Eng B J Eng Manuf 231(12):2159–2172CrossRefGoogle Scholar
  9. 9.
    Bani-Ismail B, Baghdadi Y (2018) A literature review on service identification challenges in service oriented architecture. In: International Conference on Knowledge Management in Organizations, pp 203–214Google Scholar
  10. 10.
    Cardoso A, Simões P (2012) Cloud computing: concepts, technologies and challenges. Commun Comput Inf Sci 248 CCIS:127–136Google Scholar
  11. 11.
    Bouzary H, Chen FF (2018) Service optimal selection and composition in cloud manufacturing: a comprehensive survey. Int J Adv Manuf Technol:1–14Google Scholar
  12. 12.
    Wang L, Guo C, Li Y, Du B, Guo S (2017) An outsourcing service selection method using ANN and SFLA algorithms for cement equipment manufacturing enterprises in cloud manufacturing. J Ambient Intell Humaniz Comput:1–15Google Scholar
  13. 13.
    Cheng Y, Tao F, Liu Y, Zhao D, Zhang L, Xu L (2013) Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system. Proc Inst Mech Eng B J Eng Manuf 227(12):1901–1915CrossRefGoogle Scholar
  14. 14.
    Ghomi EJ, Rahmani AM, Qader NN (2019) Cloud manufacturing: challenges, recent advances, open research issues, and future trends. Int J Adv Manuf Technol:1–27Google Scholar
  15. 15.
    Li Z, Barenji AV, Huang GQ (2018) Toward a blockchain cloud manufacturing system as a peer to peer distributed network platform. Robot Comput Integr Manuf 54:133–144CrossRefGoogle Scholar
  16. 16.
    Huang B, Li C, Tao F (2014) A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system. Enterp Inf Syst 8(4):445–463CrossRefGoogle Scholar
  17. 17.
    Li F, Zhang L, Liu Y, Laili Y (2018) QoS-Aware Service Composition in Cloud Manufacturing: A Gale-Shapley Algorithm-Based Approach. IEEE Trans Syst Man Cybern Syst 99:1–12Google Scholar
  18. 18.
    Akbaripour H, Houshmand M, Van Woensel T, Mutlu N (2015) Cloud manufacturing service selection optimization and scheduling with transportation considerations : mixed-integer programming models. Int J Adv Manuf Technol 95(1–4):1–31Google Scholar
  19. 19.
    Zhou J, Yao X (2017) Hybrid teaching--learning-based optimization of correlation-aware service composition in cloud manufacturing. Int J Adv Manuf Technol 91(9–12):3515–3533CrossRefGoogle Scholar
  20. 20.
    Zhang M, Liu L, Liu S (2016) Genetic algorithm based QoS-aware service composition in multi-cloud. Proc - 2015 IEEE Conf Collab Internet Comput. CIC 2015, pp 113–118, .Google Scholar
  21. 21.
    Chen Y, Huang J, Lin C, Shen X (2016) Multi-objective service composition with QoS dependencies. IEEE Trans Cloud Comput PP(99):1Google Scholar
  22. 22.
    Zhou J, Yao X (2017) A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. Int J Prod Res 55(16):4765–4784CrossRefGoogle Scholar
  23. 23.
    Shehu U, Safdar G, Epiphaniou G (2016) Fruit fly optimization algorithm for network-aware web service composition in the cloud. Int J Adv Comput Sci Appl 1(7):1–11Google Scholar
  24. 24.
    Liu Z-Z, Chu D-H, Song C, Xue X, Lu B-Y (2016) Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition. Inf Sci (Ny) 326:315–333CrossRefGoogle Scholar
  25. 25.
    Bharath Bhushan S, Pradeep Reddy CH (2016) A Qos aware cloud service composition algorithm for geo-distributed multi cloud domain. Int J Intell Eng Syst 9(4):147–156Google Scholar
  26. 26.
    Seghir F, Khababa A (2016) A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition. J Intell Manuf:1–20Google Scholar
  27. 27.
    Jula A, Othman Z, Sundararajan E (2015) Imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition. Expert Syst Appl 42(1):135–145CrossRefGoogle Scholar
  28. 28.
    Zhou J, Yao X (2017) DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing. Int J Adv Manuf Technol 90(1–4):1085–1103CrossRefGoogle Scholar
  29. 29.
    Lu Y, Xu X (2017) A semantic web-based framework for service composition in a cloud manufacturing environment. J Manuf Syst 42:69–81CrossRefGoogle Scholar
  30. 30.
    Xiang F, Xu L, Jiang G (2016) Green manufacturing service composition in cloud manufacturing system: an introduction. In: Industrial Electronics and Applications (ICIEA), 2016 IEEE 11th Conference on, pp 1988–1993Google Scholar
  31. 31.
    Assari M, Delaram J, Valilai OF (2018) Mutual manufacturing service selection and routing problem considering customer clustering in cloud manufacturing. Prod Manuf Res 7:0–19Google Scholar
  32. 32.
    Xiang F, Jiang GZ, Xu LL, Wang NX (2016) The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system. Int J Adv Manuf Technol 84(1–4):59–70CrossRefGoogle Scholar
  33. 33.
    Liu B, Zhang Z (2017) QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups. Int J Adv Manuf Technol 88(9–12):2757–2771CrossRefGoogle Scholar
  34. 34.
    Zheng H, Feng Y, Tan J (2016) A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. Int J Adv Manuf Technol 84(1–4):371–379CrossRefGoogle Scholar
  35. 35.
    Liu Y, Wang L, Wang XV, Xu X, Zhang L (2018) Scheduling in cloud manufacturing: state-of-the-art and research challenges. Int J Prod Res:1–26Google Scholar
  36. 36.
    Guo H, Zhang L, Tao F, Ren L, Luo YL (2010) Research on the measurement method of flexibility of resource service composition in cloud manufacturing. Adv Mater Res 139:1451–1454CrossRefGoogle Scholar
  37. 37.
    Zhang L, Guo H, Tao F, Luo YL, Si N (2010) Flexible management of resource service composition in cloud manufacturing. In: 2010 IEEE International Conference on Industrial Engineering and Engineering Management, pp 2278–2282Google Scholar
  38. 38.
    Zhang L, Luo Y, Tao F, Li BH, Ren L, Zhang X, Guo H, Cheng Y, Hu A, Liu Y (2014) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst 8(2):167–187CrossRefGoogle Scholar
  39. 39.
    Tao F, Zhang L, Venkatesh VC, Luo Y, Cheng Y (2011) Cloud manufacturing: a computing and service-oriented manufacturing model. Proc Inst Mech Eng B J Eng Manuf 225(10):1969–1976CrossRefGoogle Scholar
  40. 40.
    Tao F, Zhang L, Liu Y, Cheng Y, Wang L, Xu X (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng 137(4):40912CrossRefGoogle Scholar
  41. 41.
    Tao F, Cheng Y, Da Xu L, Zhang L, Li BH (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Ind Informatics 10(2):1435–1442CrossRefGoogle Scholar
  42. 42.
    Li T, He T, Wang Z, Zhang Y (2018) An Approach to Iot Service Optimal Composition for Mass Customization on Cloud Manufacturing. IEEE Access 6:50572–50586CrossRefGoogle Scholar
  43. 43.
    Que Y, Zhong W, Chen H, Chen X, Ji X (2018) Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing. Int J Adv Manuf Technol 96(9–12):4455–4465CrossRefGoogle Scholar
  44. 44.
    Liu Y, Xu X, Zhang L, Tao F (2016) An extensible model for multitask-oriented service composition and scheduling in cloud manufacturing. J Comput Inf Sci Eng 16(4):41009CrossRefGoogle Scholar
  45. 45.
    Cardoso J, Sheth A, Miller J, Arnold J, Kochut K (2004) Quality of service for workflows and web service processes. Web Semant Sci Serv Agents World Wide Web 1(3):281–308CrossRefGoogle Scholar
  46. 46.
    Bouzary H, Chen FF, Krishnaiyer K (2018) Service matching and selection in cloud manufacturing: a state-of-the-art review. Procedia Manuf 26:1128–1136CrossRefGoogle Scholar
  47. 47.
    Souri A, Rahmani AM, Jafari Navimipour N (2018) Formal verification approaches in the web service composition: a comprehensive analysis of the current challenges for future research. Int J Commun Syst:e3808CrossRefGoogle Scholar
  48. 48.
    Asghari P, Rahmani AM, Javadi HHS (2018) Service composition approaches in IoT: a systematic review. J Netw Comput Appl 120:61–77CrossRefGoogle Scholar
  49. 49.
    Vakili A, Navimipour NJ (2017) Comprehensive and systematic review of the service composition mechanisms in the cloud environments. J Netw Comput Appl 81:24–36CrossRefGoogle Scholar
  50. 50.
    Wang L, Shen J, Yong J (2012) A survey on bio-inspired algorithms for web service composition. In: Computer Supported Cooperative Work in Design (CSCWD), 2012 IEEE 16th International Conference on, pp 569–574Google Scholar
  51. 51.
    Garriga M, Mateos C, Flores A, Cechich A, Zunino A (2016) RESTful service composition at a glance: a survey. J Netw Comput Appl 60:32–53CrossRefGoogle Scholar
  52. 52.
    Lemos AL, Daniel F, Benatallah B (2016) Web service composition: a survey of techniques and tools. ACM Comput Surv 48(3):33Google Scholar
  53. 53.
    Jula A, Sundararajan E, Othman Z (2014) Cloud computing service composition: a systematic literature review. Expert Syst Appl 41(8):3809–3824CrossRefGoogle Scholar
  54. 54.
    Kowsalya S, Gowthami A, Harees S, Vijayanand V (2017) Semantic web service composition-a basic survey. Int J Eng Sci 5126Google Scholar
  55. 55.
    Syu Y, Ma S-P, Kuo J-Y, FanJiang Y-Y (2012) A survey on automated service composition methods and related techniques. In: Services Computing (SCC), 2012 IEEE Ninth International Conference on, pp 290–297Google Scholar
  56. 56.
    Jatoth C, Gangadharan GR, Buyya R (2017) Computational intelligence based QoS-aware web service composition: a systematic literature review. IEEE Trans Serv Comput 10(3):475–492CrossRefGoogle Scholar
  57. 57.
    Cook DJ, Greengold NL, Ellrodt AG, Weingarten SR (1997) The relation between systematic reviews and practice guidelines. Ann Intern Med 127(3):210–216CrossRefGoogle Scholar
  58. 58.
    Kitchenham B (2004) Procedures for performing systematic reviews, Keele, UK, Keele Univ, vol 33, no. TR/SE-0401, p 28Google Scholar
  59. 59.
    Kupiainen E, Mäntylä M, Itkonen J (2015) Using metrics in Agile and Lean Software Development - {a} systematic literature review of industrial studies. Inf Softw Technol 62:143–163CrossRefGoogle Scholar
  60. 60.
    Pourghebleh B, Jafari Navimipour N (2019) Towards efficient data collection mechanisms in the vehicular ad hoc networks. Int J Commun Syst:e3893CrossRefGoogle Scholar
  61. 61.
    Souri A, Navimipour NJ, Rahmani AM (2018) Formal verification approaches and standards in the cloud computing: a comprehensive and systematic review. Comput Stand Interfaces 58:1–22CrossRefGoogle Scholar
  62. 62.
    Neghabi AA, Navimipour NJ, Hosseinzadeh M, Rezaee A (2018) Load balancing mechanisms in the software defined networks: a systematic and comprehensive review of the literature. IEEE Access 6:14159–14178CrossRefGoogle Scholar
  63. 63.
    Tao F, LaiLi Y, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inf 9(4):2023–2033CrossRefGoogle Scholar
  64. 64.
    Cao Y, Wang S, Kang L, Gao Y (2016) A TQCS-based service selection and scheduling strategy in cloud manufacturing. Int J Adv Manuf Technol 82(1–4):235–251CrossRefGoogle Scholar
  65. 65.
    Iravani R (2012) Real-time simulation of large HVDC-AC grids. Int J Adv Manuf Technol 84(1–4):59–70Google Scholar
  66. 66.
    Xiang F, Hu Y, Yu Y, Wu H (2014) QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. Cent Eur J Oper Res 22(4):663–685zbMATHCrossRefGoogle Scholar
  67. 67.
    Zhou J, Yao X (2017) Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition. Appl Intell 47(3):721–742CrossRefGoogle Scholar
  68. 68.
    Chen F, Dou R, Li M, Wu H (2016) A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing. Comput Ind Eng 99:423–431CrossRefGoogle Scholar
  69. 69.
    Yongdong P (2018) Bi-level programming optimization method for cloud manufacturing service composition based on harmony search. J Comput Sci 27:462–468CrossRefGoogle Scholar
  70. 70.
    Akbaripour H, Houshmand M (2018) Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm. Neural Comput & Applic:1–22Google Scholar
  71. 71.
    Jin H, Yao X, Chen Y (2017) Correlation-aware QoS modeling and manufacturing cloud service composition. J Intell Manuf 28(8):1947–1960CrossRefGoogle Scholar
  72. 72.
    Li C, Guan J, Liu T, Ma N, Zhang J (2018) An autonomy-oriented method for service composition and optimal selection in cloud manufacturing. Int J Adv Manuf Technol 96(5–8):2583–2604CrossRefGoogle Scholar
  73. 73.
    Xu B, Qi J, Hu X, Leung KS, Sun Y, Xue Y (2018) Self-adaptive bat algorithm for large scale cloud manufacturing service composition. Peer-to-Peer Netw Appl 11(5):1115–1128CrossRefGoogle Scholar
  74. 74.
    Zhou J, Yao X (2017) A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. Int J Adv Manuf Technol 88(9–12):3371–3387CrossRefGoogle Scholar
  75. 75.
    Lu Y, Xu X (2016) Knowledge-based service composition in an open cloud manufacturing environment. J Manuf Syst 42:69–81CrossRefGoogle Scholar
  76. 76.
    Zhou J, Yao X (2017) Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing. Appl Soft Comput J 56:379–397CrossRefGoogle Scholar
  77. 77.
    Lartigau J, Xu X, Nie L, Zhan D (2015) Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved artificial bee colony optimisation algorithm. Int J Prod Res 53(14):4380–4404CrossRefGoogle Scholar
  78. 78.
    Li F, Zhang L, Liu Y, Laili Y, Tao F (2017) A clustering network-based approach to service composition in cloud manufacturing. Int J Comput Integr Manuf 30(12):1331–1342CrossRefGoogle Scholar
  79. 79.
    Wang Y, Dai Z, Zhang W, Zhang S, Xu Y, Chen Q (2018) Urgent task-aware cloud manufacturing service composition using two-stage biogeography-based optimisation. Int J Comput Integr Manuf 31(10):1034–1047CrossRefGoogle Scholar
  80. 80.
    Liu W, Liu B, Sun D, Li Y, Ma G (2013) Study on multi-task oriented services composition and optimisation with the “Multi-Composition for Each Task” pattern in cloud manufacturing systems. Int J Comput Integr Manuf 26(8):786–805CrossRefGoogle Scholar
  81. 81.
    Akbaripour H, Houshmand M, van Woensel T, Mutlu N (2018) Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models. Int J Adv Manuf Technol 95(1–4):43–70CrossRefGoogle Scholar
  82. 82.
    Al-Masri E, Mahmoud QH (2007) QoS-based discovery and ranking of Web services. In: Proceedings - International Conference on Computer Communications and Networks, ICCCN, pp 529–534Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Young Researchers and Elite Club, Tabriz BranchIslamic Azad UniversityTabrizIran
  2. 2.Young Researchers and Elite Club, Urmia BranchIslamic Azad UniversityUrmiaIran
  3. 3.Department of Computer Engineering, Tabriz BranchIslamic Azad UniversityTabrizIran

Personalised recommendations