Advances in Manufacturing

, Volume 7, Issue 4, pp 374–388 | Cite as

Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing

  • Zhao-Hui Liu
  • Zhong-Jie WangEmail author
  • Chen Yang


Cloud manufacturing is a new kind of networked manufacturing model. In this model, manufacturing resources are organized and used on demand as market-oriented services. These services are highly uncertain and focus on users. The information between service demanders and service providers is usually incomplete. These challenges make the resource scheduling more difficult. In this study, an iterative double auction mechanism is proposed based on game theory to balance the individual benefits. Resource demanders and providers act as buyers and sellers in the auction. Resource demanders offer a price according to the budget, the delivery time, preference, and the process of auction. Meanwhile, resource providers ask for a price according to the cost, maximum expected profit, optimal reservation price, and the process of auction. A honest quotation strategy is dominant for a participant in the auction. The mechanism is capable of guaranteeing the economic benefits among different participants in the market with incomplete information. Furthermore, the mechanism is helpful for preventing harmful market behaviors such as speculation, cheating, etc. Based on the iterative double auction mechanism, manufacturing resources are optimally allocated to users with consideration of multiple objectives. The auction mechanism is also incentive compatibility.


Cloud manufacturing Resource scheduling Multi-objective optimization Iterative double auction Incentive compatibility 

List of symbols


Asked price


Bidding price set


Bidding price


Execution cost

c, cr

Number of iteration




Exponential function


Distribution function


Density function


Index of demander


Index of provider


Index of task submitted by a demander


Index of resource provided by a provider


Logistics time


Logistics cost


Logistics load


Occupied cost


Occupied time


Relationship vector


Partnership between demander and provider


Comprehensive quality


Probability of a demander adopting the strategy greater than valuation


Reservation price


Strategy space


Execution time


Average period of each iteration


Ready time


Initial moment


Moment of the bidding


Net benefit of demander


Expected net benefit of a demander with bidding price b0


Expected net benefit of a demander with bidding price b


Net benefit of provider


Expected net benefit of a provider with asked price a0


Expected net benefit of a provider with asked price a


Transaction value

x, y

Index of subtask


Maximum expected profit margin

α, γ

Subjective factor


Zoom factor


Finance budget


Set of subtasks that have not been matched with resources

\(\hat{\varPhi }\)

Cost of resource


Game participants

Influence of delivery time


Priority vector of task


Quantity of candidate resources


Delivery time


Set of subtasks after a subtask along the longest past to the end subtask


Delivery time of subtask




Expected payment




Influence of demander preference


Probability of an event




Workload of subtask


Execution time of resource


  1. 1.
    Li BH, Zhang L, Wang SL et al (2010) Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manufac Syst 16(1):1–7Google Scholar
  2. 2.
    Li BH, Zhang L, Ren L et al (2011) Further discussion on cloud manufacturing. Comput Integr Manufac Syst 17(3):449–457Google Scholar
  3. 3.
    Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86CrossRefGoogle Scholar
  4. 4.
    Voinov N, Chernorutsky I, Drobintsev P et al (2017) An approach to net-centric control automation of technological processes within industrial IoT systems. Adv Manuf 5(4):388–393CrossRefGoogle Scholar
  5. 5.
    Tao F, Cheng Y, Li DX et al (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE T Ind Inform 10(2):1435–1442CrossRefGoogle Scholar
  6. 6.
    Ren L, Zhang L, Tao F et al (2015) Cloud manufacturing: from concept to practice. Enterp Inf Syst 9(2):186–209CrossRefGoogle Scholar
  7. 7.
    Wang Y, Ma HS, Yang JH et al (2017) Industry 4.0: a way from mass customization to mass personalization production. Adv Manuf 5(4):311–320CrossRefGoogle Scholar
  8. 8.
    Zhang L, Luo Y, Tao F et al (2014) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst 8(2):167–187CrossRefGoogle Scholar
  9. 9.
    Wang T, Guo S, Lee CG (2014) Manufacturing task semantic modeling and description in cloud manufacturing system. Int J Adv Manuf Tech 71(9/12):2017–2031CrossRefGoogle Scholar
  10. 10.
    Mazar AM, Yazdian N, Kovacevic R (2018) Hybrid laser/arc welding of thick high-strength steel in different configurations. Adv Manuf 6(2):176–188CrossRefGoogle Scholar
  11. 11.
    Yamato S, Yamada Y, Nakanishi K et al (2018) Integrated in-process chatter monitoring and automatic suppression with adaptive pitch control in parallel turning. Adv Manuf 6(3):291–300CrossRefGoogle Scholar
  12. 12.
    Liu Y, Xu X, Zhang L et al (2017) Workload-based multitask scheduling in cloud manufacturing. Robot Comput Integr Manuf 45:3–20CrossRefGoogle Scholar
  13. 13.
    Zhou L, Zhang L, Zhao C et al (2018) Diverse task scheduling for individualized requirements in cloud manufacturing. Enterp Inf Syst 12(3):300–318CrossRefGoogle Scholar
  14. 14.
    Tunc LT, Ozsahin O (2018) Use of inverse stability solutions for identification of uncertainties in the dynamics of machining processes. Adv Manuf 6(3):308–318CrossRefGoogle Scholar
  15. 15.
    Laili YJ, Tao F, Zhang L et al (2012) A study of optimal allocation of computing resources in cloud manufacturing systems. Int J Adv Manuf Tech 63(5/8):671–690CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Li W, Zhu C, Yang LT et al (2017) Subtask scheduling for distributed robots in cloud manufacturing. IEEE Syst J 11(2):941–950CrossRefGoogle Scholar
  18. 18.
    Wang SL, Zhu ZQ, Kang L (2016) Resource allocation model in cloud manufacturing. Proc Inst Mech Eng Part C-J Mech Eng Sci 230(10):1726–1741CrossRefGoogle Scholar
  19. 19.
    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 Tech 88(9/12):3371–3387CrossRefGoogle Scholar
  20. 20.
    Cao Y, Wang S, Kang L et al (2016) A TQCS-based service selection and scheduling strategy in cloud manufacturing. Int J Adv Manuf Tech 82(1/4):235–251CrossRefGoogle Scholar
  21. 21.
    Cheng Z, Zhan D, Zhao X et al (2014) Multitask oriented virtual resource integration and optimal scheduling in cloud manufacturing. J Appl Mat 2014(7):1–9Google Scholar
  22. 22.
    Lin YK, Chong CS (2017) Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. J Intell Manuf 28(5):1189–1201CrossRefGoogle Scholar
  23. 23.
    Jiang H, Yi J, Chen S et al (2016) A multi-objective algorithm for task scheduling and resource allocation in cloudbased disassembly. J Manuf Syst 41:239–255CrossRefGoogle Scholar
  24. 24.
    Wang J, Gong B, Liu H et al (2015) Multidisciplinary approaches to artificial swarm intelligence for heterogeneous computing and cloud scheduling. Appl Intell 43(3):662–675CrossRefGoogle Scholar
  25. 25.
    Ren L, Cui J, Wei Y et al (2016) Research on the impact of service provider cooperative relationship on cloud manufacturing platform. Int J Adv Manuf Tech 86(5/8):2279–2290CrossRefGoogle Scholar
  26. 26.
    Nielsen I, Dang QV, Bocewicz G et al (2017) A methodology for implementation of mobile robot in adaptive manufacturing environments. J Intell Manuf 28(5):1171–1188CrossRefGoogle Scholar
  27. 27.
    Wang L, Cai JC, Li M (2016) An adaptive multi-population genetic algorithm for job-shop scheduling problem. Adv Manuf 4(2):142–149CrossRefGoogle Scholar
  28. 28.
    Yuan MH, Deng K, Chaovalitwongse WA et al (2017) Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing. Optim Methods Softw 32(3):581–593MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Wang L, Guo S, Li X et al (2018) Distributed manufacturing resource selection strategy in cloud manufacturing. Int J Adv Manuf Tech 94(9/12):3375–3388CrossRefGoogle Scholar
  30. 30.
    Romp G (1997) Game theory: introduction and applications. Oxford University Press, OxfordGoogle Scholar
  31. 31.
    Zhang Y, Wang J, Liu S et al (2017) Game theory based real-time shop floor scheduling strategy and method for cloud manufacturing. Int J Intell Syst 32(4):437–463CrossRefGoogle Scholar
  32. 32.
    Erdman AG, Sandor GN (1997) Mechanism design: analysis and synthesis. Prentice Hall, EnglewoodGoogle Scholar
  33. 33.
    Vickrey W (1961) Counter speculation, auctions, and competitive sealed tenders. J Financ 16(1):8–37MathSciNetCrossRefGoogle Scholar
  34. 34.
    Riley JG, Samuelson WF (1981) Optimal auctions. Am Econ Rev 71(3):381–392Google Scholar
  35. 35.
    McAfee RP (1992) A dominant strategy double auction. J Econ Theory 56(2):434–450MathSciNetzbMATHCrossRefGoogle Scholar
  36. 36.
    Nezarat A, Dastghaibifard GH (2015) Efficient nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction. PLoS ONE 10(10):e0138424CrossRefGoogle Scholar
  37. 37.
    Fard HM, Prodan R, Fahringer T (2013) A truthful dynamic workflow scheduling mechanism for commercial multicloud environments. IEEE T Parall Distr 24(6):1203–1212CrossRefGoogle Scholar
  38. 38.
    Samimi P, Teimouri Y, Mukhtar M (2016) A combinatorial double auction resource allocation model in cloud computing. Inf Sci 357:201–216CrossRefGoogle Scholar
  39. 39.
    Peng W, Guo W, Shao HY (2017) Price formation mechanism in cloud manufacturing system for small and medium enterprices. Comput Integr Manufac Syst 23(3):650–660Google Scholar

Copyright information

© Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Electronics and Information EngineeringTongji UniversityShanghaiPeople’s Republic of China
  2. 2.College of Engineering Science and TechnologyShanghai Ocean UniversityShanghaiPeople’s Republic of China

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