Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-25T06:09:20.443Z Has data issue: false hasContentIssue false

Autonomous resource allocation of smart workshop for cloud machining orders

Published online by Cambridge University Press:  07 October 2020

Jizhuang Hui
Affiliation:
Institute of Smart Manufacturing Systems, Chang'an University, Middle-section of Nan'er Huan Road Xi'an, Xi'an, Shaanxi Province710064, China Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, Xi'an, Shaanxi710064, China
Jingyuan Lei*
Affiliation:
Institute of Smart Manufacturing Systems, Chang'an University, Middle-section of Nan'er Huan Road Xi'an, Xi'an, Shaanxi Province710064, China Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, Xi'an, Shaanxi710064, China
Kai Ding
Affiliation:
Institute of Smart Manufacturing Systems, Chang'an University, Middle-section of Nan'er Huan Road Xi'an, Xi'an, Shaanxi Province710064, China Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, Xi'an, Shaanxi710064, China
Fuqiang Zhang
Affiliation:
Institute of Smart Manufacturing Systems, Chang'an University, Middle-section of Nan'er Huan Road Xi'an, Xi'an, Shaanxi Province710064, China Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, Xi'an, Shaanxi710064, China
Jingxiang Lv
Affiliation:
Institute of Smart Manufacturing Systems, Chang'an University, Middle-section of Nan'er Huan Road Xi'an, Xi'an, Shaanxi Province710064, China Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, Xi'an, Shaanxi710064, China
*
Author for correspondence: Jingyuan Lei, E-mail: leijy94@chd.edu.cn

Abstract

In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

Type
Research Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chen, J, Huang, GQ, Wang, JQ and Yang, C (2019) A cooperative approach to service booking and scheduling in cloud manufacturing. European Journal of Operational Research.273, 861873.CrossRefGoogle Scholar
Di, S and Wang, C-L (2013) Dynamic optimization of multiattribute resource allocation in self-organizing clouds. IEEE Transactions on Parallel and Distributed Systems 24, 464478.CrossRefGoogle Scholar
Ding, K, Lei, J, Chan, FTS, Hui, J, Zhang, F and Wang, Y (2020) Hidden Markov model-based autonomous manufacturing task orchestration in smart shop floors. Robotics and Computer-Integrated Manufacturing 61.doi:10.1016/j.rcim.2019.101845.CrossRefGoogle Scholar
Endo, PT, De Almeida Palhares, AV, Pereira, NN, Goncalves, GE, Sadok, D, Kelner, J, Melander, B and Mångs, JE (2011) Resource allocation for distributed cloud: concepts and research challenges. IEEE Network 25, 4246.CrossRefGoogle Scholar
Fisher, O, Watson, N, Porcu, L, Bacon, D, Rigley, M and Gomes, RL (2018) Cloud manufacturing as a sustainable process manufacturing route. Journal of Manufacturing Systems 47, 5368.CrossRefGoogle Scholar
Gaines, DM and Regli, WC (2003) Guest editorial: Special issue: New artificial intelligence paradigms for manufacturing. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, 171.CrossRefGoogle Scholar
Ghosh, AK, Ullah, AMMS and Kubo, A (2019) Hidden Markov model-based digital twin construction for futuristic manufacturing systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, 317331.CrossRefGoogle Scholar
Guan, C, Zhang, Z, Liu, S and Gong, J (2019) Multi-objective particle swarm optimization for multi-workshop facility layout problem. Journal of Manufacturing Systems 53, 3248.CrossRefGoogle Scholar
Guo, L, Wang, S, Kang, L and Cao, Y (2015) Agent-based manufacturing service discovery method for cloud manufacturing. International Journal of Advanced Manufacturing Technology 81, 21672181.CrossRefGoogle Scholar
Henzel, R and Herzwurm, G (2018) Cloud Manufacturing: a state-of-the-art survey of current issues. Procedia CIRP 72, 947952.CrossRefGoogle Scholar
Kumar, N and Saxena, S (2015) A preference-based resource allocation in cloud computing systems. Procedia Computer Science 57, 104111.CrossRefGoogle Scholar
Li, X (2019) Introduction to Intelligent Manufacturing. Beijing: China Machine Press.Google Scholar
Liu, Q and Ding, D (2017) The Road to Intelligent Manufacturing - Expert Wisdom. Beijing: China Machine Press.Google Scholar
Liu, Y and Xu, X (2017) Industry 4.0 and cloud manufacturing: a comparative analysis. Journal of Manufacturing Science and Engineering, Transactions of the ASME 139.10.1115/1.4034667CrossRefGoogle Scholar
Mubarok, K, Xu, X, Ye, X, Zhong, RY and Lu, Y (2018) Manufacturing service reliability assessment in cloud manufacturing. Procedia CIRP 72, 940946.CrossRefGoogle Scholar
Pan, XY, Ma, JZ and Zhao, DZ (2019) Study on pricing behaviour and capacity allocation of cloud manufacturing service platform. Cluster Computing 22, 1470114707.CrossRefGoogle Scholar
Qin, J, Liu, Y, Grosvenor, R, Lacan, F and Jiang, Z (2020) Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production 245.10.1016/j.jclepro.2019.118702CrossRefGoogle Scholar
Ren, L, Zhang, L, Tao, F, Zhao, C, Chai, X and Zhao, X (2014) Cloud manufacturing: from concept to practice. doi:10.1080/17517575.2013.839055.CrossRefGoogle Scholar
Song, KY, Wang, M, Liu, LM, Zhu, GL and Zhang, YF (2019) Toward intelligent manufacturing workshop modeling and validation of a resource-driven mechanism-based info-interconnect model. Journal of Computing and Information Science in Engineering 19, 4.CrossRefGoogle Scholar
Tang, J, So, DKC, Alsusa, E, Hamdi, KA and Shojaeifard, A (2015) Resource allocation for energy efficiency optimization in heterogeneous networks. IEEE Journal on Selected Areas in Communications 33, 21042117.CrossRefGoogle Scholar
Tao, F, Cheng, Y, Da Xu, L, Zhang, L and Li, BH (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Transactions on Industrial Informatics 10, 14351442.Google Scholar
Wang, B, Chen, L, Chen, X, Zhang, X and Yang, D (2011) Resource allocation optimization for device-to-device communication underlaying cellular networks. In 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), pp. 1–6.CrossRefGoogle Scholar
Wu, J, Zhu, Q, An, Q, Chu, J and Ji, X (2016) Resource allocation based on context-dependent data envelopment analysis and a multi-objective linear programming approach. Computers & Industrial Engineering 101, 8190.CrossRefGoogle Scholar
Yang, XS (2010) Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation 2, 7884.CrossRefGoogle Scholar
Zhang, F and Li, J (2018) An improved particle swarm optimization algorithm for integrated scheduling model in AGV-served manufacturing systems. Journal of Advanced Manufacturing Systems 17, 375390.CrossRefGoogle Scholar
Zhang, H, Liu, Q, Chen, X, Zhang, D and Leng, J (2017 a) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5, 2690126911.CrossRefGoogle Scholar
Zhang, Y, Qian, C, Lv, J and Liu, Y (2017 b) Agent and cyber-physical system based. IEEE Transactions on Industrial Informatics 13, 737747.CrossRefGoogle Scholar
Zhang, Y, Zhang, G, Liu, Y and Hu, D (2017 c) Research on services encapsulation and virtualization access model of machine for cloud manufacturing. Journal of Intelligent Manufacturing 28, 11091123.CrossRefGoogle Scholar
Zhang, Y, Guo, Z, Lv, J and Liu, Y (2018 a) A framework for smart production-logistics systems based on CPS and industrial IoT. IEEE Transactions on Industrial Informatics 14, 40194032.CrossRefGoogle Scholar
Zhang, Y, Zhu, Z and Lv, J (2018 b) CPS-based smart control model for shopfloor material handling. IEEE Transactions on Industrial Informatics 14, 17641775.CrossRefGoogle Scholar
Zhang, C, Zhou, G, Li, H and Cao, Y (2020) manufacturing blockchain of things for the configuration of a data-and knowledge-driven digital twin manufacturing cell. IEEE Internet of Things Journal. doi: 10.1109/JIOT.2020.3005729.CrossRefGoogle Scholar
Zheng, L, Chen, Q and Gu, J (2012) Research on modeling and searching of networked manufacturing resources. Machinery Design and Manufacture 8, 254255.Google Scholar
Zhou, G, Yuan, S, Lu, Q and Xiao, X (2018 a) A carbon emission quantitation model and experimental evaluation for machining process considering tool wear condition. International Journal of Advanced Manufacturing Technology 98, 565577.CrossRefGoogle Scholar
Zhou, G, Zhou, C, Lu, Q, Tian, C and Xiao, Z (2018 b) Feature-based carbon emission quantitation strategy for the part machining process. International Journal of Computer Integrated Manufacturing 31, 406425.CrossRefGoogle Scholar