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Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories

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Abstract

Cyber-Physical System (CPS) is one of the most promising directions of Industry 4.0 smart manufacturing. Abundant manufacturing data and information are available for decision-makers in real-time thanks to the application of various frontier technologies in CPS. However, the inherent complexity and uncertainty of manufacturing optimization still plague scholars and practitioners and impede further progress of smart manufacturing. The production planning and scheduling is such a complex and stochastic problem that has received considerable research attention. Whereas how to leverage the strengths of CPS for breaking the bottleneck of complexity and uncertainty, is still a question that needs further exploration. This paper proposes a novel “divide and conquer” approach, Spatial–Temporal Out-Of-Order execution (ST-OOO), for achieving real-time planning and scheduling in cyber-physical factories. ST-OOO divides the space and time scopes of a factory into finite areas and intervals to reduce complexity and localize uncertainties so that the original complex optimization problem is decomposed into a set of subproblems with different spatial and temporal characteristics. These small-size subproblems can be assembled using data and information visibility and traceability, and then solved in a rolling spatiotemporal manner to generate a global solution. A case study shows that ST-OOO has a well-balanced and more stable performance compared to traditional strategies. Sensitivity analysis is carried out to study the impacts of spatial and temporal scales on the results.

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MATLAB R2019a with custom code.

References

  • Balakrishnan, J., & Cheng, C. H. (2007). Multi-period planning and uncertainty issues in cellular manufacturing: A review and future directions. European Journal of Operational Research, 177(1), 281–309.

    Article  Google Scholar 

  • Bitran, G. R., Haas, E. A., & Hax, A. C. (1982). Hierarchical production planning: A two-stage system. Operations Research, 30(2), 232–251.

    Article  Google Scholar 

  • Brah, S. A., & Hunsucker, J. L. (1991). Branch and bound algorithm for the flow shop with multiple processors. European Journal of Operational Research, 51(1), 88–99.

    Article  Google Scholar 

  • Brah, S. A., & Wheeler, G. E. (1998). Comparison of scheduling rules in a flow shop with multiple processors: A simulation. Simulation, 71(5), 302–311.

    Article  Google Scholar 

  • Chen, J., Wang, M., Kong, X. T., Huang, G. Q., Dai, Q., & Shi, G. (2019). Manufacturing synchronization in a hybrid flowshop with dynamic order arrivals. Journal of Intelligent Manufacturing, 30(7), 2659–2668.

    Article  Google Scholar 

  • Dempster, M. A. H., Fisher, M., Jansen, L., Lageweg, B., Lenstra, J. K., & Rinnooy Kan, A. (1981). Analytical evaluation of hierarchical planning systems. Operations Research, 29(4), 707–716.

    Article  Google Scholar 

  • Efthymiou, K., Mourtzis, D., Pagoropoulos, A., Papakostas, N., & Chryssolouris, G. (2016). Manufacturing systems complexity analysis methods review. International Journal of Computer Integrated Manufacturing, 29(9), 1025–1044.

    Article  Google Scholar 

  • Fang, W., & Zheng, L. (2020). Shop floor data-driven spatial–temporal verification for manual assembly planning. Journal of Intelligent Manufacturing, 31(4), 1003–1018.

    Article  Google Scholar 

  • Guo, D., Zhong, R. Y., Lin, P., Lyu, Z., Rong, Y., & Huang, G. Q. (2020). Digital twin-enabled graduation intelligent manufacturing system for fixed-position assembly islands. Robotics and Computer-Integrated Manufacturing, 63, 101917.

    Article  Google Scholar 

  • Hax, A. C., & Meal, H. C. (1973). Hierarchical integration of production planning and scheduling. Sloan School of Management: Massachusetts Institute of Technology.

    Google Scholar 

  • Keller, B., & Bayraksan, G. (2009). Scheduling jobs sharing multiple resources under uncertainty: A stochastic programming approach. IIE Transactions, 42(1), 16–30.

    Article  Google Scholar 

  • Kia, H., Davoudpour, H., & Zandieh, M. (2010). Scheduling a dynamic flexible flow line with sequence-dependent setup times: a simulation analysis. International Journal of Production Research, 48(14), 4019–4042.

    Article  Google Scholar 

  • Komaki, G., Teymourian, E., & Kayvanfar, V. (2016). Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems. International Journal of Production Research, 54(4), 963–983.

    Article  Google Scholar 

  • Kong, X. T., Luo, H., Huang, G. Q., & Yang, X. (2019). Industrial wearable system: The human-centric empowering technology in Industry 4.0. Journal of Intelligent Manufacturing, 30(8), 2853–2869.

    Article  Google Scholar 

  • Kurz, M. E., & Askin, R. G. (2004). Scheduling flexible flow lines with sequence-dependent setup times. European Journal of Operational Research, 159(1), 66–82.

    Article  Google Scholar 

  • Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544(7648), 23–25.

    Article  Google Scholar 

  • Li, Z., & Ierapetritou, M. G. (2010). Rolling horizon based planning and scheduling integration with production capacity consideration. Chemical Engineering Science, 65(22), 5887–5900.

    Article  Google Scholar 

  • Li, M., Xu, G., Lin, P., & Huang, G. Q. (2019). Cloud-based mobile gateway operation system for industrial wearables. Robotics and Computer-Integrated Manufacturing, 58, 43–54.

    Article  Google Scholar 

  • Li, M., Jiang, M., Lyu, Z., Chen, Q., Wu, H., & Huang, G. Q. (2020). Spatial-temporal finite element analytics for cyber-physical system-enabled smart factory: Application in hybrid flow shop. Procedia Manufacturing, 51, 1229–1236.

    Article  Google Scholar 

  • Lin, P., Li, M., Kong, X., Chen, J., Huang, G. Q., & Wang, M. (2018a). Synchronisation for smart factory-towards IoT-enabled mechanisms. International Journal of Computer Integrated Manufacturing, 31(7), 624–635.

    Article  Google Scholar 

  • Lin, P., Shen, L., Zhao, Z., & Huang, G. Q. (2018b). Graduation manufacturing system: synchronization with IoT-enabled smart tickets. Journal of Intelligent Manufacturing, 30, 2885.

    Article  Google Scholar 

  • Liu, Y., & Karimi, I. (2008). Scheduling multistage batch plants with parallel units and no interstage storage. Computers Chemical Engineering, 32(4–5), 671–693.

    Article  Google Scholar 

  • Luo, H., Yang, X., & Wang, K. (2019). Synchronized scheduling of make to order plant and cross-docking warehouse. Computers & Industrial Engineering, 138, 106108.

    Article  Google Scholar 

  • Menezes, G. C., Mateus, G. R., & Ravetti, M. G. (2016). A hierarchical approach to solve a production planning and scheduling problem in bulk cargo terminal. Computers & Industrial Engineering, 97, 1–14.

    Article  Google Scholar 

  • O’Reilly, S., Kumar, A., & Adam, F. (2015). The role of hierarchical production planning in food manufacturing SMEs. International Journal of Operations & Production Management, 35(10), 1362–1385.

    Article  Google Scholar 

  • Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182.

    Article  Google Scholar 

  • Qin, W., Zhang, J., & Song, D. (2018). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing, 29(4), 891–904.

    Article  Google Scholar 

  • Rahmani, D., & Ramezanian, R. (2016). A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: A case study. Computers & Industrial Engineering, 98, 360–372.

    Article  Google Scholar 

  • Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1–18.

    Article  Google Scholar 

  • Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics, 14(11), 4724–4734.

    Article  Google Scholar 

  • Sridharan, V., Berry, W. L., & Udayabhanu, V. (1987). Freezing the master production schedule under rolling planning horizons. Management Science, 33(9), 1137–1149.

    Article  Google Scholar 

  • Tao, F., Cheng, J., & Qi, Q. (2017). IIHub: An industrial Internet-of-Things hub toward smart manufacturing based on cyber-physical system. IEEE Transactions on Industrial Informatics, 14(5), 2271–2280.

    Article  Google Scholar 

  • Torkaman, S., Ghomi, S. F., & Karimi, B. (2017). Multi-stage multi-product multi-period production planning with sequence-dependent setups in closed-loop supply chain. Computers & Industrial Engineering, 113, 602–613.

    Article  Google Scholar 

  • Udoka, S. J. (1991). Automated data capture techniques: A prerequisite for effective integrated manufacturing systems. Computers & Industrial Engineering, 21(1–4), 217–221.

    Article  Google Scholar 

  • Vandevelde, A., Hoogeveen, H., Hurkens, C., & Lenstra, J. K. (2005). Lower bounds for the head-body-tail problem on parallel machines: A computational study of the multiprocessor flow shop. INFORMS Journal on Computing, 17(3), 305–320.

    Article  Google Scholar 

  • Wang, L., & Haghighi, A. (2016). Combined strength of holons, agents and function blocks in cyber-physical systems. Journal of Manufacturing Systems, 40, 25–34.

    Article  Google Scholar 

  • Wang, S., & Liu, M. (2013). A heuristic method for two-stage hybrid flow shop with dedicated machines. Computers & Operations Research, 40(1), 438–450.

    Article  Google Scholar 

  • Weng, W., Wei, X., & Fujimura, S. (2012). Dynamic routing strategies for JIT production in hybrid flow shops. Computers & Operations Research, 39(12), 3316–3324.

    Article  Google Scholar 

  • Wittrock, R. J. (1988). An adaptable scheduling algorithm for flexible flow lines. Operations Research, 36(3), 445–453.

    Article  Google Scholar 

  • Yang, H., Kumara, S., Bukkapatnam, S. T., & Tsung, F. (2019). The internet of things for smart manufacturing: A review. IISE Transactions, 51, 1190.

    Article  Google Scholar 

  • Zhong, R. Y., Dai, Q., Qu, T., Hu, G., & Huang, G. Q. (2013). RFID-enabled real-time manufacturing execution system for mass-customization production. Robotics and Computer-Integrated Manufacturing, 29(2), 283–292.

    Article  Google Scholar 

  • Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5), 616–630.

    Article  Google Scholar 

  • Zienkiewicz, O. C., Taylor, R. L., & Zhu, J. (2013). The finite element method: Its basis and fundamentals. Amsterdam: Elsevier.

    Google Scholar 

Download references

Funding

The authors would like to acknowledge partial financial supports from funding sources, including HKSAR RGC GRF Project (17203518), the 2019 Guangdong Special Support Talent Program – Innovation and Entrepreneurship Leading Team (China) (2019BT02S593), and 2018 Guangzhou Leading Innovation Team Program (201909010006).

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, 8/F, Haking Wong Building, HKU, Pokfulam Road, Pok Fu Lam, Hong Kong, China

    Mingxing Li, Ray Y. Zhong & George Q. Huang

  2. School of Intelligent Systems Science and Engineering, Institute of Physical Internet, Jinan University (Zhuhai Campus), Zhuhai, 519070, China

    Ting Qu

Authors
  1. Mingxing Li
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  2. Ray Y. Zhong
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  3. Ting Qu
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  4. George Q. Huang
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Contributions

ML: Conceptualization, Methodology, Formal analysis and investigation, Writing - original draft preparation. RYZ: Conceptualization, Writing - review and editing TQ: Conceptualization, Writing - review and editing. GQH: Conceptualization, Methodology, Supervision, Resources, Funding acquisition.

Corresponding author

Correspondence to George Q. Huang.

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Conflict of interest

The authors declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. There are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address (gqhuang@hku.hk) which is accessible by the Corresponding Author and which has been configured to accept emails from the editorial office.

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Li, M., Zhong, R.Y., Qu, T. et al. Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories. J Intell Manuf 33, 1355–1372 (2022). https://doi.org/10.1007/s10845-020-01727-2

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  • DOI: https://doi.org/10.1007/s10845-020-01727-2

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