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A hybrid manufacturing scheduling optimization strategy in collaborative edge computing

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Abstract

In the era of Industry 4.0, hybrid scheduling based on collaboration edge computing has the advantages of high computational power and low latency, which can meet the needs of smart manufacturing scheduling. However, existing scheduling schemes cannot strike a balance between algorithm complexity and performance. To address the above challenges, we propose a hybrid scheduling model based on collaborative edge computing. Our proposed scheduling model integrates cloud-edge scheduling (coarse-grained phase) and edge-edge scheduling (fine-grained phase) to meet dynamic and real-time requirements. Our work is as follows: (i) first, we use the Johnson Bellman algorithm (JBA) to deter- mine the task decomposition order in the coarse-grained phase; (ii) second, we propose an improved Q-network scheduling method (DQN) for the job assignment problem in the fine-grained phase; (iii) finally, we simulate the cooperation of the two phases to significantly reduce the maximum completion time through simulation experiments. The experimental results show that the method can significantly reduce the dynamic scheduling time and achieve the effect of real-time scheduling.

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References

  1. Afrin M, Jin J, Rahman A, Tian Y, Kulkarni A (2019) Multi-objective resource allocation for edge cloud based robotic workflow in smart factory. Future Gener Comput Syst 97:119–130

    Article  Google Scholar 

  2. Jian C, Ping J, Zhang M (2020) A cloud edge-based two-level hybrid scheduling learning model in cloud manufacturing. Int J Prod Res 3:1–15

    Google Scholar 

  3. Shellshear E, Berlin R, Carlson JS (2015) Maximizing smart factory systems by incrementally updating point clouds. IEEE Comput Graphics Appl 35(2):62–69

    Article  Google Scholar 

  4. Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  Google Scholar 

  5. Abualigah L, Elaziz MA, Sumari P, Zong WG, Gandomi AH (2021) Reptile search algorithm (rsa): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191(11):116158

    Google Scholar 

  6. Ovelade ON, Ezugwu AE (2021) Ebola optimization search algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. In: 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, pp 1–10

  7. Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic opti- mization algorithm inspired by fifa world cup competitions: theory and its application in pid designing for avr system. J Control Autom Electr Syst 27(4):419–440

    Article  Google Scholar 

  8. Zhang G, Xiao C, Razmjooy N (2022) Optimal parameter extraction of pem fuel cells by meta-heuristics. Int J Ambient Energy 43(1):2510–2519

    Article  CAS  Google Scholar 

  9. Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570

    Article  MathSciNet  Google Scholar 

  10. Razmjooy N, Ashourian M, Foroozandeh Z (2020) Metaheuristics and optimization in computer and electrical engineering. Springer, Berlin

    Google Scholar 

  11. Liu Y, Zhang L, Wang L, Xiao Y, Xu X, Wang M (2019) A framework for scheduling in cloud manufacturing with deep reinforcement learning. In: 2019 IEEE 17th international conference on industrial informatics (INDIN)

  12. Li L, Ota K, Dong M (2018) Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans Ind Inf 14(10):4665–4673

    Article  Google Scholar 

  13. Nahhas A, Lang S, Bosse S, Turowski K (2018) Toward adaptive manufacturing: Scheduling problems in the context of industry 4.0. In: 2018 Sixth international conference on enterprise systems (ES), pp 108–115

  14. Zhang J, Ding G, Zou Y, Qin S, Fu J (2017) Review of job shop scheduling research and its new perspectives under industry 4.0. J Intell Manuf

  15. Feng Y, Wang Y, Zheng H, Mi S, Tan J (2018) A framework of joint energy provisioning and manufacturing scheduling in smart industrial wireless rechargeable sensor networks. Sensors 18(8):2591

    Article  PubMed  PubMed Central  Google Scholar 

  16. Fang Y, Peng C, Lou P, Zhou Z, Yan J (2019) Digital-twin based job shop scheduling towards smart manufacturing. IEEE Trans Ind Inf 99:1–1

    Google Scholar 

  17. Balande U, Shrimankar D (2022) A modified teaching learning metaheuristic algorithm with opposite-based learning for permutation flow-shop scheduling problem. Evol Intell pp 1–23 (2022)

  18. Masdari M, Zangakani M (2020) Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities. J Supercomput 76(1):499–535

    Article  Google Scholar 

  19. Yuan H, Bi J, Zhou M (2019) Multiqueue scheduling of heterogeneous tasks with bounded response time in hybrid green iaas clouds. IEEE Trans Ind Inf 15(10):5404–5412

    Article  Google Scholar 

  20. Beegom ASA, Rajasree MS (2019) Integer-pso: a discrete pso algorithm for task scheduling in cloud computing systems. Evol Intell

  21. Lin CC, Deng DJ, Chih YL, Chiu HT (2019) Smart manufacturing scheduling with edge computing using multiclass deep Q network. IEEE Trans Ind Inf 15(7):4276–4284

    Article  Google Scholar 

  22. Li X, Wan J, Dai HN, Imran M, Xia M, Celesti A (2019) A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. IEEE Trans Ind Inf 15(7):4225–4234

    Article  Google Scholar 

  23. Feng J, Liu Z, Wu C, Ji Y (2017) AVE: autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Trans Veh Technol 66(12):10660–10675

    Article  Google Scholar 

  24. Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–169

    Article  Google Scholar 

  25. Pane YP, Nageshrao SP, Kober J, Babuˇska R (2019) Reinforcement learn- ing based compensation methods for robot manipulators. Eng Appl Artif Intell 78:236–247

    Article  Google Scholar 

  26. Dong T, Xue F, Xiao C, Li J (2020) Task scheduling based on deep reinforcement learning in a cloud manufacturing environment. Concurrency Comput Pract Exp 32(11):e5654

    Article  Google Scholar 

  27. Leng J, Yan D, Liu Q, Xu K, Zhao JL, Shi R, Wei L, Zhang D, Chen X (2020) Manuchain: combining permissioned blockchain with a holistic optimization model as bi-level intelligence for smart manufacturing. IEEE Trans Syst Man Cybern Syst 50(1):182–192

    Article  Google Scholar 

  28. Chang KC, Chu KC, Wang HC, Lin YC, Pan JS (2020) Agent-based middleware framework using distributed cps for improving resource uti- lization in smart city. Fut Gener Comput Syst 108:445–453

    Article  Google Scholar 

  29. Zhang Y, Xu X, Liu A, Lu Q, Tao F (2019) Blockchain-based trust mech- anism for iot-based smart manufacturing system. IEEE Trans Comput Soc Syst 6(6):1386–1394

    Article  Google Scholar 

  30. Johnson SM (1954) Optimal two- and three-stage production schedules with setup times included. Naval Res Logist Quart 1(1):61–68

    Article  Google Scholar 

  31. Hsu CJ, Kuo WH, Yang DL, Chern MS (2006) Minimizing the makespan in a two-stage flowshop scheduling problem with a function constraint on alternative machines. J Mar Sci Technol 14(4):213–217

    Article  Google Scholar 

  32. Xiong Y, Huang S, Min W, She J, Jiang K (2017) A johnson’s-rule-based genetic algorithm for two-stage-task scheduling problem in data-centers of cloud computing. IEEE Trans Cloud Comput 7(3):597–610

    Article  Google Scholar 

  33. Luo J, Xing K, Zhou M, Li X, Wang X (2015) Deadlock-free scheduling of automated manufacturing systems using petri nets and hybrid heuristic search. IEEE Trans Syst Man Cybern Syst 45(3):530–541

    Article  Google Scholar 

  34. Ma Y, Zhu W, Benton MG, Romagnoli J (2019) Continuous control of a polymerization system with deep reinforcement learning. J Process Control 75:40–47

    Article  CAS  Google Scholar 

  35. Moon J, Jeong J (2021) Smart manufacturing scheduling system: Dqn based on cooperative edge computing. In: 2021 15th international conference on ubiquitous information management and communication (IMCOM), pp 1–8

  36. He Y, Sick B (2021) Clear: an adaptive continual learning framework for regression tasks. AI Perspect 3(1):1–16

    Article  Google Scholar 

  37. Naqushbandi FS, John A (2022) Sequence of actions recognition using con- tinual learning. In: 2022 Second international conference on artificial intelligence and smart energy (ICAIS), pp 858–863

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China under Grant Nos. 61672461 and 62073293.

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Author notes

  1. Xianghui Hou, Hao Xu, Lukun Bao, Meiyu Zhang and Chengfeng Jian have contributed this work equally.

Authors and Affiliations

  1. Computer Science and Technology College, ZhejiangUniversityofTechnology, Hangzhou, 310023, China

    Zhuoyang Pan, Xianghui Hou, Hao Xu, Lukun Bao, Meiyu Zhang & Chengfeng Jian

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  1. Zhuoyang Pan
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  2. Xianghui Hou
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Correspondence to Chengfeng Jian.

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Pan, Z., Hou, X., Xu, H. et al. A hybrid manufacturing scheduling optimization strategy in collaborative edge computing. Evol. Intel. 17, 1065–1077 (2024). https://doi.org/10.1007/s12065-022-00786-z

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