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Cloud manufacturing: challenges, recent advances, open research issues, and future trends

  • Einollah Jafarnejad Ghomi
  • Amir Masoud RahmaniEmail author
  • Nooruldeen Nasih Qader
ORIGINAL ARTICLE

Abstract

Cloud manufacturing (CMfg) is a new manufacturing paradigm over computer networks aiming at using distributed resources in the form of manufacturing capabilities, hardware, and software. Some modern technologies such as cloud computing, Internet of Things (IoT), service-oriented, and radio-frequency identification (RFID) play a key role in CMfg. In CMfg, all resources needed for manufacturing such as hardware, software, and manufacturing capabilities are virtualized; the services are provided by manufacturing resources. In this paper, the key characteristics, concepts, challenges, open issues, and future trends of cloud manufacturing are presented to direct the future researches. Accordingly, five directions of advances in CMfg are introduced and the articles in five categories are reviewed and analyzed: (1) studies focused on the architecture and platform design of CMfg; (2) studies concentrated on resource description and encapsulation; (3) studies focused on service selection and composition; (4) studies aimed at resource allocation and service scheduling; and (5) studies aimed at service searching and matching. The article also aims at providing a development diagram in the area of CMfg as a roadmap for future research opportunities and practice.

Keywords

Cloud manufacturing Resource virtualization Semantic web Service composition Service matching Scheduling 

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References

  1. 1.
    Ren L, Zhang L, Wang L, Tao F, Chai X (2017) Cloud manufacturing: key characteristics and applications. Int J Comput Integr Manuf 30(6):501–515CrossRefGoogle Scholar
  2. 2.
    Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86CrossRefGoogle Scholar
  3. 3.
    Ren L, Zhang L, Tao F, Zhao C, Chai X, Zhao X (2015) Cloud manufacturing: from concept to practice. Enterp Inf Syst 9(2):186–209CrossRefGoogle Scholar
  4. 4.
    Wu D, Greer MJ, Rosen DW, Schaefer D (2013) Cloud manufacturing: strategic vision and state-of-the-art. J Manuf Syst 32(4):564–579CrossRefGoogle Scholar
  5. 5.
    He W, Lida X (2015) A state-of-the-art survey of cloud manufacturing. Int J Comput Integr Manuf 28(3):239–250CrossRefGoogle Scholar
  6. 6.
    Adamson G, Wang L, Holm M, Moore P (2017) Cloud manufacturing—a critical review of recent development and future trends. Int J Comput Integr Manuf 30(4–5):347–380Google Scholar
  7. 7.
    Tao F, Lin Z, Liu Y, Cheng Y, Wang L, Xun X (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng 137(4):040912CrossRefGoogle Scholar
  8. 8.
    Tarchinskaya E, Taratukhin V, Becker J (2016) Cloud-based engineering design and manufacturing: a survey. In: Emerging trends in information systems. Springer, Cham, pp. 125–135Google Scholar
  9. 9.
    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
  10. 10.
    Li L (2018) China’s manufacturing locus in 2025: with a comparison of “made-in-China 2025” and “industry 4.0”. Technol Forecast Soc Chang 135:66–74Google Scholar
  11. 11.
    Shadroo S, Rahmani AM (2018) Systematic survey of big data and data mining in internet of things. Comput Netw 139:19–47CrossRefGoogle Scholar
  12. 12.
    Kang HS, Yeon Lee J, Choi SS, Kim H, Park JH, Ji YS, Bo HK, Do Noh S (2016) Smart manufacturing: past research, present findings, and future directions. Int J Precis Eng Manuf Green Technol 3(1):111–128CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Ren L, Zhang L, Zhao C, Chai X ( 2013) Cloud manufacturing platform: operating paradigm, functional requirements, and architecture design. In: ASME 2013 international manufacturing science and engineering conference collocated with the 41st North American manufacturing research conference. American Society of Mechanical Engineers, pp V002T02A009-V002T02A009Google Scholar
  15. 15.
    Xu X (2013) Cloud manufacturing: a new paradigm for manufacturing businesses. Aust J Multi-Discip Eng 9(2):105–116CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Yadekar Y, Shehab E, Mehnen J (2016) Taxonomy and uncertainties of cloud manufacturing. Int J Agile Syst Manag 9(1):48–66CrossRefGoogle Scholar
  18. 18.
    Tao F, LaiLi Y, Xu L, Lin Z (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
  19. 19.
    Esmaeilian B, Behdad S, Wang B (2016) The evolution and future of manufacturing: a review. J Manuf Syst 39:79–100CrossRefGoogle Scholar
  20. 20.
    Chen T, Tsai H-R (2017) Ubiquitous manufacturing: current practices, challenges, and opportunities. Robot Comput Integr Manuf 45:126–132CrossRefGoogle Scholar
  21. 21.
    Liu K, Zhong P, Zeng Q, Li D, Li S (2017) Application modes of cloud manufacturing and program analysis. J Mech Sci Technol 31(1):157–164CrossRefGoogle Scholar
  22. 22.
    Wang Y, Ma S, Ren L (2014) A security framework for cloud manufacturing. In: ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, pp V001T04A022-V001T04A022Google Scholar
  23. 23.
    Buckholtz B, Ragai I, Wang L (2015) Cloud manufacturing: current trends and future implementations. J Manuf Sci Eng 137(4):040902CrossRefGoogle Scholar
  24. 24.
    Qu T, Lei SP, Wang ZZ, Nie DX, Chen X, Huang GQ (2016) IoT-based real-time production logistics synchronization system under smart cloud manufacturing. Int J Adv Manuf Technol 84(1–4):147–164CrossRefGoogle Scholar
  25. 25.
    Wu D, Rosen DW, Wang L, Schaefer D (2015) Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Comput Aided Des 59:1–14CrossRefGoogle Scholar
  26. 26.
    Wang XV, Xun WX (2013) ICMS: a cloud-based manufacturing system. In: Cloud manufacturing. Springer, London, pp 1–22Google Scholar
  27. 27.
    Liu X, Li Y, Wang L (2015) A cloud manufacturing architecture for complex parts machining. J Manuf Sci Eng 137(6):061009CrossRefGoogle Scholar
  28. 28.
    Yang C, Shen W, Lin T, Wang X (2016) A hybrid framework for integrating multiple manufacturing clouds. Int J Adv Manuf Technol 86(1–4):895–911CrossRefGoogle Scholar
  29. 29.
    Zhang Y, Zhang G, Liu Y, Hu D (2017) Research on services encapsulation and virtualization access model of machine for cloud manufacturing. J Intell Manuf 28(5):1109–1123Google Scholar
  30. 30.
    Luo Y, Zhang L, Tao F, Ren L, Liu Y, Zhang Z (2013) A modeling and description method of multidimensional information for manufacturing capability in cloud manufacturing system. Int J Adv Manuf Technol 69(5–8):961–975CrossRefGoogle Scholar
  31. 31.
    Tao F, Zuo Y, Li Da X, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10(2):1547–1557CrossRefGoogle Scholar
  32. 32.
    Xu W, Yu J, Zhou Z, Xie Y, Pham DT, Ji C (2015) Dynamic modeling of manufacturing equipment capability using condition information in cloud manufacturing. J Manuf Sci Eng 137(4):040907CrossRefGoogle Scholar
  33. 33.
    Yu J, Zhou Z, Xu W (2014) Dynamic modeling of manufacturing equipment capability in cloud manufacturing. In: ASME 2014 international manufacturing science and engineering conference collocated with the JSME 2014 international conference on materials and processing and the 42nd North American manufacturing research conference. American Society of Mechanical Engineers, pp V001T04A018-V001T04A018Google Scholar
  34. 34.
    Wang L, Yao Y, Yang X, Chen D (2016) Multi agent based additive manufacturing cloud platform. In: Cloud Computing and Big Data Analysis (ICCCBDA), 2016 IEEE International Conference on, IEEE, pp 290-295Google Scholar
  35. 35.
    Lu Y, Shao Q, Singh C, Xu X, Ye X (2014) Ontology for manufacturing resources in a cloud environment. Int J Manuf Res 9(4):448–469CrossRefGoogle Scholar
  36. 36.
    Liu Z-Z, Song C, Chu DH, Hou ZW, Peng WP (2017) An approach for multipath cloud manufacturing services dynamic composition. Int J Intell Syst 32(4):371–393Google Scholar
  37. 37.
    Jula A, Sundararajan E, Othman Z (2014) Cloud computing service composition: a systematic literature review. Expert Syst Appl 41(8):3809–3824CrossRefGoogle Scholar
  38. 38.
    Lu Y, Xun X (2017) A semantic web-based framework for service composition in a cloud manufacturing environment. J Manuf Syst 42:69–81CrossRefGoogle Scholar
  39. 39.
    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
  40. 40.
    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):2757–2771Google Scholar
  41. 41.
    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):4765–4784Google Scholar
  42. 42.
    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):371–379Google Scholar
  43. 43.
    Zhang Y, Zhang G, Qu T, Liu Y, Zhong RY (2017) Analytical target cascading for optimal configuration of cloud manufacturing services. J Clean Prod 151:330–343CrossRefGoogle Scholar
  44. 44.
    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 56:379–397CrossRefGoogle Scholar
  45. 45.
    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):1331–1342Google Scholar
  46. 46.
    Liu Y, Xu X, Lin Z, Tao F (2016) An extensible model for multitask-oriented service composition and scheduling in cloud manufacturing. J Comput Inf Sci Eng 16(4):041009CrossRefGoogle Scholar
  47. 47.
    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):3515–3533Google Scholar
  48. 48.
    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
  49. 49.
    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
  50. 50.
    Kumar RR, Mishra S, Kumar C (2017) Prioritizing the solution of cloud service selection using integrated MCDM methods under fuzzy environment. J Supercomput:1–31Google Scholar
  51. 51.
    Zhang W, Yang Y, Zhang S, Yu D, Yangbing X (2016) A new manufacturing service selection and composition method using improved flower pollination algorithm. Math Probl Eng 2016:1–12Google Scholar
  52. 52.
    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
  53. 53.
    Xiang F, GuoZhang Jiang LLX, 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
  54. 54.
    Seghir F, Khababa A (2018) A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition. J Intell Manuf 29(8):1773–1792Google Scholar
  55. 55.
    Karimi MB, Isazadeh A, Rahmani AM (2017) QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm. J Supercomput 73(4):1387–1415CrossRefGoogle Scholar
  56. 56.
    Liu Y, Xu X, Zhang L, Wang L, Zhong RY (2017) Workload-based multi-task scheduling in cloud manufacturing. Robot Comput Integr Manuf 45:3–20CrossRefGoogle Scholar
  57. 57.
    Wang S-l, Zhu Z-q, Kang L (2016) Resource allocation model in cloud manufacturing. Proc Inst Mech Eng C J Mech Eng Sci 230(10):1726–1741CrossRefGoogle Scholar
  58. 58.
    Wu S-y, Zhang P, Li F, Feng G, Pan Y (2016) A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems. J Cent South Univ 23:421–429CrossRefGoogle Scholar
  59. 59.
    Zhou L, Zhang L (2016) A dynamic task scheduling method based on simulation in cloud manufacturing. In: Asian simulation conference. Springer, Singapore, pp 20–24Google Scholar
  60. 60.
    Li W, Zhu C, Yang LT, Shu L, Ngai ECH, Ma Y (2017) Subtask scheduling for distributed robots in cloud manufacturing. IEEE Syst J 11(2):941–950Google Scholar
  61. 61.
    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
  62. 62.
    Cheng Z, Zhan D, Zhao X, Wan H (2014) Multitask oriented virtual resource integration and optimal scheduling in cloud manufacturing. J Appl Math 2014:1–9Google Scholar
  63. 63.
    Laili Y, Zhang L, Tao F (2011) Energy adaptive immune genetic algorithm for collaborative design task scheduling in cloud manufacturing system. In: Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on, IEEE, pp 1912-1916Google Scholar
  64. 64.
    Jian CF, Wang Y (2014) Batch task scheduling-oriented optimization modelling and simulation in cloud manufacturing. Int J Simul Model 13(1):93–101MathSciNetCrossRefGoogle Scholar
  65. 65.
    Barenji AV, Barenji RV, Roudi D, Hashemipour M (2017) A dynamic multi-agent-based scheduling approach for SMEs. Int J Adv Manuf Technol 89(9–12):3123–3137Google Scholar
  66. 66.
    Cui J, Ren L, Zhang L, Wu Q. (2015) An optimal allocation method for virtual resource considering variable metrics of cloud manufacturing service. In: ASME 2015 International Manufacturing Science and Engineering Conference, American Society of Mechanical Engineers, pp V002T04A013-V002T04A013Google Scholar
  67. 67.
    Thekinen J, Panchal JH (2017) Resource allocation in cloud-based design and manufacturing: a mechanism design approach. J Manuf Syst 43:327–338CrossRefGoogle Scholar
  68. 68.
    Lartigau J, Nie L, Xu X, Zhan D, Mou T (2012) Scheduling methodology for production services in cloud manufacturing. In: Service Sciences (IJCSS), 2012 International Joint Conference on, IEEE, pp 34-39Google Scholar
  69. 69.
    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
  70. 70.
    Zhou L, Lin Z, Zhao C, Laili Y, Lida X (2018) Diverse task scheduling for individualized requirements in cloud manufacturing. Enterp Inf Syst 12(3):300–318CrossRefGoogle Scholar
  71. 71.
    Jiang H, Yi J, Chen S, Zhu X (2016) A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly. J Manuf Syst 41:239–255CrossRefGoogle Scholar
  72. 72.
    Yuan M, Deng K, Chaovalitwongse WA, Cheng S (2017) Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing. Optim Methods Softw 32(3):581–593MathSciNetzbMATHCrossRefGoogle Scholar
  73. 73.
    Li X, Song J, Huang B (2016) A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics. Int J Adv Manuf Technol 84(1–4):119–131CrossRefGoogle Scholar
  74. 74.
    Yang C, Wang ZJ (2013) Research on the cloud manufacturing service discovery for industry manufacturing system based on ontology. Adv Mater Res 712:2639–2643. Trans tech publicationsCrossRefGoogle Scholar
  75. 75.
    Li H, Zhang L, Jiang R (2014) Study of manufacturing cloud service matching algorithm based on OWL-S. In: Control and Decision Conference (2014 CCDC), The 26th Chinese, IEEE, pp 4155-4160Google Scholar
  76. 76.
    Yuan M, Deng K, Chaovalitwongse WA (2017) Manufacturing resource modeling for cloud manufacturing. Int J Intell Syst 32(4):414–436Google Scholar
  77. 77.
    Wang W, Liu F (2012) The research of cloud manufacturing resource discovery mechanism. In: Computer Science & Education (ICCSE), 2012 7th International Conference on, IEEE, pp 188-191Google Scholar
  78. 78.
    Li H-F, Zhao L, Zhang B-H, Li J-Q (2015) Service matching and composition considering correlations among cloud services. In: Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, IEEE, pp 509–514Google Scholar
  79. 79.
    Tai LJ, Ru Fu H, Chen CW, Huang YD (2013) Manufacturing resources and demand intelligent matching in cloud manufacturing environment. Adv Mater Res 616:2101–2104. Trans tech publicationsGoogle Scholar
  80. 80.
    Cheng Y, Tao F, Zhao D, Zhang L (2017) Modeling of manufacturing service supply–demand matching hypernetwork in service-oriented manufacturing systems. Robot Comput Integr Manuf 45:59–72CrossRefGoogle Scholar
  81. 81.
    Cheng Y, Tao F, Xu L, Zhao D (2018) Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and internet of things. Enterp Inf Syst 12(7):780–797Google Scholar
  82. 82.
    Sheng B, Zhang C, Yin X, Lu Q, Cheng Y, Xiao T, Liu H (2016) Common intelligent semantic matching engines of cloud manufacturing service based on OWL-S. Int J Adv Manuf Technol 84(1–4):103–118CrossRefGoogle Scholar
  83. 83.
    Guo L, Wang S, Kang L, Cao Y (2015) Agent-based manufacturing service discovery method for cloud manufacturing. Int J Adv Manuf Technol 81(9–12):2167–2181CrossRefGoogle Scholar
  84. 84.
    Ghomi, EJ, Rahmani AM, Qader NN (2017) Load-balancing algorithms in cloud computing: a survey. J Netw Comput Appl 88:50–71Google Scholar
  85. 85.
    Mittal S, Khan MA, Romero D, Wuest T (2017) Smart manufacturing: characteristics, technologies and enabling factors. Proc Inst Mech Eng B J Eng Manuf 0954405417736547Google Scholar
  86. 86.
    Kusiak A (2018) Smart manufacturing. Int J Prod Res 56(1–2):508–517CrossRefGoogle Scholar
  87. 87.
    Mourtzis D, Vlachou E, Milas N, Xanthopoulos N (2016) A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring. Procedia CIRP 41:655–660CrossRefGoogle Scholar
  88. 88.
    Liu X, Qiu X, Chen B, Huang K (2012) Cloud-based simulation: the state-of-the-art computer simulation paradigm. In: Principles of Advanced and Distributed Simulation (PADS), 2012 ACM/IEEE/SCS 26th Workshop on, IEEE, pp 71-74Google Scholar
  89. 89.
    Chen T, Chiu M-C (2017) Development of a cloud-based factory simulation system for enabling ubiquitous factory simulation. Robot Comput Integr Manuf 45:133–143CrossRefGoogle Scholar
  90. 90.
    Zawadzki P, Żywicki K (2016) Smart product design and production control for effective mass customization in the industry 4.0 concept. Manag Prod Eng Rev 7(3):105–112Google Scholar
  91. 91.
    Riungu-Kalliosaari L, Taipale O, Smolander K, Richardson I (2016) Adoption and use of cloud-based testing in practice. Softw Qual J 24(2):337–364CrossRefGoogle Scholar
  92. 92.
    Lee J, Bagheri B, Kao H-A (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23CrossRefGoogle Scholar
  93. 93.
    Chang H-C, Liu T-K (2017) Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms. J Intell Manuf 28(8):1973–1986CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer ScienceUniversity of Human DevelopmentSulaymaniyahIraq
  3. 3.Computer Science DepartmentUniversity of SulaimaniSulaymaniyahIraq

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