Skip to main content
SpringerLink
Log in

Manufacturing task data chain-driven production logistics trajectory analysis and optimization decision making method

  • Published:
Advances in Manufacturing Aims and scope Submit manuscript

Abstract

Production logistics (PL) is considered as a critical factor that affects the efficiency and cost of production operations in discrete manufacturing systems. To effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits, this study proposes a PL trajectory analysis and optimization decision making method driven by a manufacturing task data chain (MTDC). First, the manufacturing task chain (MTC) is defined to characterize the discrete production process of a product. To handle manufacturing big data, the MTC data paradigm is designed, and the MTDC is established. Then, the logistics trajectory model is presented, where the various types of logistics trajectories are extracted using the MTC as the search engine for the MTDC. Based on this, a logistics efficiency evaluation indicator system is proposed to support the optimization decision making for the PL. Finally, a case study is applied to verify the proposed method, and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment, which can assist managers in implementing the optimization decisions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Xu K, Zhu K, Tao Y (2020) Multi-process scheduling optimization for small-batch orders. In: proceedings of the 2020 4th international conference on electronic information technology and computer engineering, pp 870‒874. https://doi.org/10.1145/3443467.3443870

  2. Yue MY, Zhou YD (2013) Progress of theoretical research-oriented multi-species small batch machining process. Appl Mech Mater 364:470–473

    Article  Google Scholar 

  3. Li Q, Wei F, Zhou S (2017) Early warning systems for multi-variety and small batch manufacturing based on active learning. J Intell Fuzzy Syst 33(5):2945–2952

    Article  Google Scholar 

  4. Liu ZF, Zhang YZ, Zhang CX et al (1884) (2021) Real-time workshop digital twin scheduling platform for discrete manufacturing. J Phys: Conf Ser 1:012006. https://doi.org/10.1088/1742-6596/1884/1/012006

    Google Scholar 

  5. Qu T, Lei SP, Wang ZZ et al (2015) IoT-based real-time production logistics synchronization system under smart cloud manufacturing. Int J Adv Manuf Technol 84(1/4):147–164

    Google Scholar 

  6. Peng MJ (2017) Analysis of factors affecting manufacturing logistics costs. Mod Commer Ind 2:29–30

    Google Scholar 

  7. Balon B, Roszak M (2020) Cost-quantitative analysis of non-compliance in the internal logistics process. Prod Eng Arch 26(2):60–66

    Article  Google Scholar 

  8. Winkelhaus S, Grosse EH (2020) Logistics 4.0: a systematic review towards a new logistics system. Int J Prod Res 58(1):18–43

    Article  Google Scholar 

  9. Yang W, Li W, Cao Y et al (2020) Real-time production and logistics self-adaption scheduling based on information entropy theory. Sensors 20(16):4507. https://doi.org/10.3390/s20164507

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  10. Zhang Y, Zhang G, Wang J et al (2015) Real-time information capturing and integration framework of the internet of manufacturing things. Int J Comput Integr Manuf 28(8):811–822

    Article  Google Scholar 

  11. Cao W, Jiang P, Lu P et al (2017) Real-time data-driven monitoring in job-shop floor based on radio frequency identification. Int J Adv Manuf Technol 92(5):2099–2120

    Article  Google Scholar 

  12. Anandhi S, Anitha R, Sureshkumar V (2019) IoT enabled RFID authentication and secure object tracking system for smart logistics. Wirel Pers Commun 104(2):543–560

    Article  Google Scholar 

  13. Wang T, Qiu L, Sangaiah AK et al (2020) Edge-computing-based trustworthy data collection model in the internet of things. IEEE Internet Things J 7(5):4218–4227

    Article  Google Scholar 

  14. Zhong RY, Huang GQ, Lan S et al (2015) A big data approach for logistics trajectory discovery from RFID-enabled production data. Int J Prod Econ 165:260–272

    Article  Google Scholar 

  15. Zhong RY, Xu C, Chen C et al (2017) Big data analytics for physical internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621

    Article  ADS  Google Scholar 

  16. Zhong RY (2018) Analysis of RFID datasets for smart manufacturing shop floors. In: 2018 IEEE 15th international conference on networking, sensing and control (ICNSC). IEEE, pp 1‒4. https://doi.org/10.1109/ICNSC.2018.8361321

  17. Qu T, Thürer M, Wang J et al (2017) System dynamics analysis for an Internet-of-Things-enabled production logistics system. Int J Prod Res 55(9):2622–2649

    Article  Google Scholar 

  18. Knoll D, Reinhart G, Prüglmeier M (2019) Enabling value stream mapping for internal logistics using multidimensional process mining. Expert Syst Appl 124:130–142

    Article  Google Scholar 

  19. Tripathi AK, Sharma K, Bala M et al (2020) A parallel military-dog-based algorithm for clustering big data in cognitive industrial internet of things. IEEE Trans Industr Inf 17(3):2134–2142

    Article  Google Scholar 

  20. Luo H, Wang K, Kong XT et al (2017) Synchronized production and logistics via ubiquitous computing technology. Robot Comput Integr Manuf 45:99–115

    Article  Google Scholar 

  21. Jiang A, Chi Q, Gao J et al (2019) An integrated approach to forecasting intermittent demand for electric power materials. Comput Econ 53(4):1309–1335

    Article  Google Scholar 

  22. Ren S, Zhao X, Huang B et al (2019) A framework for shopfloor material delivery based on real-time manufacturing big data. J Ambient Intell Humaniz Comput 10(3):1093–1108

    Article  Google Scholar 

  23. Sly D, Helwig M, Hu G (2017) Improving the efficiency of large manufacturing assembly plants. Proc Manuf 11:1818–1825

    Google Scholar 

  24. Wang W, Zhang Y, Zhong RY (2020) A proactive material handling method for CPS enabled shop-floor. Robot Comput Integr Manuf 61:101849. https://doi.org/10.1016/j.rcim.2019.101849

    Article  Google Scholar 

  25. Huang B, Wang W, Ren S et al (2019) A proactive task dispatching method based on future bottleneck prediction for the smart factory. Int J Comput Integr Manuf 32(3):278–293

    Article  Google Scholar 

  26. Lu Z, Zhuang Z, Huang Z et al (2019) A framework of ment based intelligent production logistics system. Proc CIRP 83:557–562

    Article  Google Scholar 

  27. Yao F, Keller A, Ahmad M et al (2018) Optimizing the scheduling of autonomous guided vehicle in a manufacturing process. In: 2018 IEEE 16th international conference on industrial informatics (INDIN). IEEE, pp 264‒269, https://doi.org/10.1109/INDIN.2018.8471979

  28. Zhang Y, Zhang G, Du W et al (2015) An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data. Int J Prod Econ 165:282–292

    Article  Google Scholar 

  29. Kang Y, Feng G, Wang Z et al (2020) Real-time task assignment method of two-load AGV under dynamic change of goods urgency in logistics warehouse. J Phys: Conf Ser 1576(1):012055. https://doi.org/10.1088/1742-6596/1576/1/012055

    Article  Google Scholar 

  30. Qian C, Zhang Y, Jiang C et al (2020) A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing. Robot Comput Integr Manuf 61:101841. https://doi.org/10.1016/j.rcim.2019.101841

    Article  Google Scholar 

  31. Wang J, Zhang Y, Liu Y et al (2018) Multiagent and bargaining-game-based real-time scheduling for internet of things-enabled flexible job shop. IEEE Internet Things J 6(2):2518–2531

    Article  Google Scholar 

  32. Guo Z, Zhang Y, Zhao X et al (2020) CPS-based self-adaptive collaborative control for smart production-logistics systems. IEEE Trans Cybern 51(1):188–198

    Article  PubMed  Google Scholar 

  33. Zafarzadeh M, Wiktorsson M, Hauge JB et al (2019) Data-driven production logistics–an industrial case study on potential and challenges. Smart Sustain Manuf Syst 3:53–78

    Article  Google Scholar 

  34. Liu S, Wang L, Wang X et al (2020) A framework of data-driven dynamic optimization for smart production logistics. In: IFIP international conference on advances in production management systems. Springer, Cham, pp 213‒221. https://doi.org/10.1007/978-3-030-57997-5_25

  35. Guo H, Zhu Y, Zhang Y et al (2021) A digital twin-based layout optimization method for discrete manufacturing workshop. Int J Adv Manuf Technol 112(5):1307–1318

    Article  Google Scholar 

  36. Andrade-Gutierrez ES, Carranza-Bernal SY, Hernandez-Sandoval J et al (2018) Optimization in a flexible die-casting engine-head plant via discrete event simulation. Int J Adv Manuf Technol 95(9):4459–4468

    Article  Google Scholar 

  37. Pilati F, Regattieri A (2018) The impact of digital technologies and artificial intelligence on production systems in today Industry 4.0 environment. Netw Ind Q 20(2):16–20

    Google Scholar 

  38. Wang F, Liu S, Liu P et al (2006) Bridging physical and virtual worlds: complex event processing for RFID data streams. In: International conference on extending database technology. Springer, Berlin, pp 588–607. https://doi.org/10.1007/11687238_36

  39. Tiacci L (2020) Object-oriented event-graph modeling formalism to simulate manufacturing systems in the Industry 4.0 era. Simul Modell Pract Theory 99:102027. https://doi.org/10.1016/j.simpat.2019.102027

    Article  Google Scholar 

  40. Rahman H, Ahmed N, Hussain MI (2018) A QoS-aware hybrid data aggregation scheme for Internet of Things. Ann Telecommun 73(7):475–486

    Article  Google Scholar 

  41. da Silva ACF, Hirmer P, Mitschang B (2019) Model-based operator placement for data processing in iot environments. In: 2019 IEEE international conference on smart computing (SMARTCOMP). IEEE, pp 439‒443. https://doi.org/10.1109/SMARTCOMP.2019.00084

  42. Yousif A, Abdlkader HM (2019) A novel approach for reducing RFID uncertainty using variational bayesian inference. In: 2019 29th international conference on computer theory and applications (ICCTA). IEEE, pp 96‒101. https://doi.org/10.1109/ICCTA48790.2019.9478805

  43. Wu Y, Shen H, Sheng QZ (2014) A cloud-friendly RFID trajectory clustering algorithm in uncertain environments. IEEE Trans Parallel Distrib Syst 26(8):2075–2088

    Article  Google Scholar 

  44. Zhang Y, Guo Z, Lv J et al (2018) A framework for smart production-logistics systems based on CPS and industrial IoT. IEEE Trans Industr Inf 14(9):4019–4032

    Article  Google Scholar 

  45. Schiffer M, Schneider M, Laporte G (2018) Designing sustainable mid-haul logistics networks with intra-route multi-resource facilities. Eur J Oper Res 265(2):517–532

    Article  MathSciNet  Google Scholar 

  46. Bayhan H, Meißner M, Kaiser P et al (2020) Presentation of a novel real-time production supply concept with cyber-physical systems and efficiency validation by process status indicators. Int J Adv Manuf Technol 108(1):527–537

    Article  Google Scholar 

  47. Tavana M, Zareinejad M, Santos-Arteaga FJ et al (2016) A conceptual analytic network model for evaluating and selecting third-party reverse logistics providers. Int J Adv Manuf Technol 86(5):1705–1721

    Article  Google Scholar 

  48. Govindan K, Sarkis J, Palaniappan M (2013) An analytic network process-based multicriteria decision making model for a reverse supply chain. Int J Adv Manuf Technol 68(1):863–880

    Article  Google Scholar 

  49. Wang W, Yang H, Zhang Y et al (2018) IoT-enabled real-time energy efficiency optimisation method for energy-intensive manufacturing enterprises. Int J Comput Integr Manuf 31(4/5):362–379

    Article  Google Scholar 

  50. Chen W, Li SB, Huang H (2016) Active perception and management model for manufacturing data in discrete IoMT-based process. Comput Integr Manuf Syst 22:166–176

    CAS  Google Scholar 

  51. Zhang Y, Ma S, Yang H et al (2018) A big data driven analytical framework for energy-intensive manufacturing industries. J Clean Prod 197:57–72

    Article  Google Scholar 

  52. Zhou Z, Cai Y, Xiao Y et al (2018) The optimization of reverse logistics cost based on value flow analysis–a case study on automobile recycling company in China. J Intell Fuzzy Syst 34(2):807–818

    Article  Google Scholar 

  53. Liu X, Qu T, Wu Q et al (2017) Internet-of-thing-based dynamic kitting synchronization of production and logistics: analysis and solution. Ind Eng J 20(3):35. https://doi.org/10.3969/j.issn.1007-7375.e17-2005

    Article  CAS  Google Scholar 

  54. Peng J (2019) Mathematical models for logistics network optimization with uncertain data. In: Proceedings of the 2019 international conference on information technology and computer communications, pp 93‒100. https://doi.org/10.1145/3355402.3355403

Download references

Acknowledgement

Fundings were supported by The University Discipline (Professional) Top-notch Talent Academic Funding Project of Anhui Province, the General Project of National Natural Science Foundation of Anhui Province.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Lin Ling, Zhe-Ming Song, Xi Zhang, Xiao-Qiao Wang & Ming-Zhou Liu

  2. Guobo Electronics Co. Ltd., Nanjing, 211111, People’s Republic of China

    Peng-Zhou Cao

  3. School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, Jiangsu, People’s Republic of China

    Cong-Hu Liu

  4. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, 200030, People’s Republic of China

    Cong-Hu Liu

Authors
  1. Lin Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhe-Ming Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng-Zhou Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiao-Qiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cong-Hu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ming-Zhou Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Ling.

Appendix

Appendix

See Figs. A and B.

Fig. A
figure 12

Algorithm flow of LTS

Full size image
Fig. B
figure 13

Algorithm flow of SLT

Full size image

1.1 Structure of the database related to the manufacturing process and logistics data

The input data used in the case study were obtained from the MES production data. Because of the large amount of data, only the database structure related to the manufacturing process and logistics data is displayed, and detailed data records are omitted. Table A presents the relevant database tables. Figure C depicts the relationship between these database tables and the data sources (red background) for the MTDC and evaluation indicators.

Table A List of the database tables
Full size table
Fig. C
figure 14

Structure of the database

Full size image

Rights and permissions

Springer Nature or its licensor (e.g., a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ling, L., Song, ZM., Zhang, X. et al. Manufacturing task data chain-driven production logistics trajectory analysis and optimization decision making method. Adv. Manuf. 12, 185–206 (2024). https://doi.org/10.1007/s40436-023-00454-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40436-023-00454-0

Keywords

Search

Navigation