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

doi:10.1007/s40436-023-00454-0

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1): 185-206.doi: 10.1007/s40436-023-00454-0

• • 上一篇

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

Lin Ling¹, Zhe-Ming Song¹, Xi Zhang¹, Peng-Zhou Cao², Xiao-Qiao Wang¹, Cong-Hu Liu^3,4, Ming-Zhou Liu¹

1. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, People's Republic of China;
2. Guobo Electronics Co. Ltd., Nanjing, 211111, People's Republic of China;
3. School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, Jiangsu, People's Republic of China;
4. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China

收稿日期:2022-09-13 修回日期:2022-11-03 发布日期:2024-03-14
通讯作者: Lin Ling,E-mail:linglin@hfut.edu.cn E-mail:linglin@hfut.edu.cn
作者简介:Lin Ling Associate Professor, School of Mechanical Engineering, Department of Industrial Engineering, Hefei University of Technology, Hefei, Anhui, China. She received Ph.D. degree in Industrial Engineering from Hefei University of Technology in 2014. The main research interests include production logistics and scheduling, manufacturing execution system, and intelligent manufacturing;
Zhe-Ming Song Master degree candidate, School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui, China. He received B.E. degree in Industrial Engineering from Hefei University of Technology in 2020. The research interest focuses on production logistics and scheduling;
Xi Zhang Doctor, Lecturer, School of Mechanical Engineering, Department of Industrial Engineering, Hefei University of Technology, Hefei, Anhui, China. He received Ph.D. degree in Industrial Engineering from Hefei University of Technology in 2015. The main research interests include production scheduling, manufacturing execution system, and intelligent manufacturing;
Peng-Zhou Cao Software R&D Engineer, Guobo Electronics Co., Ltd., Nanjing, Jiangsu, China. He received M.E degree in Industrial Engineering from Hefei University of Technology in 2021. The research interest focuses on production logistics and management information system;
Xiao-Qiao Wang Doctor, Lecturer, School of Mechanical Engineering, Department of Industrial Engineering, Hefei University of Technology, Hefei, Anhui, China. He received Ph.D. degree in Industrial Engineering from Hefei University of Technology in 2015. The main research interests include online quality control, manufacturing execution system, and intelligent manufacturing;
Cong-Hu Liu Doctor, Professor, School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, Jiangsu, China. He received Ph.D. degree in Industrial Engineering from Hefei University of Technology in 2016. The main research interests include re-manufacturing quality control and re-manufacturing intelligent assembly system;
Ming-Zhou Liu Doctor, Professor, Doctoral Supervisor, School of Mechanical Engineering, Department of Industrial Engineering, Hefei University of Technology, Hefei, Anhui, China. He received Ph.D. degree in Mechanical Engineering from Kharkiv Polytechnic Institute, Ukraine in 1999. The main research interests include manufacturing execution system, intelligent manufacturing and ergonomics.
基金资助:
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.

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

Lin Ling¹, Zhe-Ming Song¹, Xi Zhang¹, Peng-Zhou Cao², Xiao-Qiao Wang¹, Cong-Hu Liu^3,4, Ming-Zhou Liu¹

1. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, People's Republic of China;
2. Guobo Electronics Co. Ltd., Nanjing, 211111, People's Republic of China;
3. School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, Jiangsu, People's Republic of China;
4. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China

Received:2022-09-13 Revised:2022-11-03 Published:2024-03-14
Contact: Lin Ling,E-mail:linglin@hfut.edu.cn E-mail:linglin@hfut.edu.cn
Supported by:
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.

摘要/Abstract

摘要： 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00454-0

关键词: Production logistics (PL), Logistics trajectory analysis, Logistics optimization, Data driven, Manufacturing task data chain (MTDC)

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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00454-0

Key words: Production logistics (PL), Logistics trajectory analysis, Logistics optimization, Data driven, Manufacturing task data chain (MTDC)

Lin Ling, Zhe-Ming Song, Xi Zhang, Peng-Zhou Cao, Xiao-Qiao Wang, Cong-Hu Liu, Ming-Zhou Liu. Manufacturing task data chain-driven production logistics trajectory analysis and optimization decision making method[J]. Advances in Manufacturing, 2024, 12(1): 185-206.

参考文献

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
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
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
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
6 Peng MJ (2017) Analysis of factors affecting manufacturing logistics costs. Mod Commer Ind 2:29–30
7 Balon B, Roszak M (2020) Cost-quantitative analysis of non-compliance in the internal logistics process. Prod Eng Arch 26(2):60–66
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
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
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
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
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
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
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
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
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
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
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
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
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
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
23 Sly D, Helwig M, Hu G (2017) Improving the efficiency of large manufacturing assembly plants. Proc Manuf 11:1818–1825
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
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
26 Lu Z, Zhuang Z, Huang Z et al (2019) A framework of ment based intelligent production logistics system. Proc CIRP 83:557–562
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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

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

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