Skip to main content
SpringerLink
Log in

Optimization of logistics flow management through big data analytics for sustainable development and environmental cycles

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In today’s global landscape, efficient logistics management is crucial for fostering sustainable development across industries. Integrating big data analytics into logistics operations has emerged as a transformative approach to boost efficiency, minimize waste, and mitigate environmental impact. This paper aims to explore the significance of utilizing big data analytics to optimize logistics management for sustainable development. The current challenges in logistics management primarily revolve around four key aspects: the efficiency of goods delivery, raw material costs, one-stop solutions, and service evaluation. These challenges persist in traditional logistics management due to their strong independence. Therefore, this paper is devoted to the optimization of logistics flow management through the lens of sustainable development and ecological cycles. Initially, by considering the Sustainable Development logistics management mode and survey data from 302 logistics enterprises, improvements are made in the four aspects. Subsequently, a four-element analysis function is designed, comprising factors for efficiency improvement, cost reduction, comprehensive optimization, and service enhancement. This function, which aims to assess logistics operations thoroughly, is a crucial component of the research. It includes components for enhancing services, cutting costs, improving efficiency, and conducting comprehensive optimization. MATLAB 21.0 b software is utilized for model analysis, primarily focusing on Volar model area analysis. The aim is to identify and categorize new optimization directions for SD logistics management mode based on the collected data and analyzed samples. The effectiveness of the SD logistics management mode is verified by observing model results and trends. Ultimately, the paper demonstrates the relevance of optimizing logistics management mode by significantly improving the efficiency of goods delivery, reducing raw material costs, enhancing one-stop solutions, and evaluating service-sharing information.

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
Fig. 12
Fig. 13

Similar content being viewed by others

Data and material availability

Access to the data supporting the research findings can be obtained by contacting the corresponding author for further information or inquiries.

References

  • Ali M, Yin B, Kumar A, Sheikh AM et al (2020) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese control conference (CCC). IEEE, pp 7406–7411. https://doi.org/10.23919/CCC50068.2020.9188843

  • Ali M, Yin B, Bilal H et al (2023) Advanced efficient strategy for detection of dark objects based on spiking network with multi-box detection. Multimedia Tools Appl. https://doi.org/10.1007/s11042-023-16852-2

    Article  Google Scholar 

  • Bibri ES, Krogstie J, Kaboli A, Alahi A (2023) Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: a comprehensive systematic review. Environ Sci Ecotechnol 19:100330

    Article  Google Scholar 

  • Bilal H, Yin B, Kumar A et al (2023a) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27:4029–4039. https://doi.org/10.1007/s00500-023-07923-5

    Article  Google Scholar 

  • Bilal H, Yin B, Aslam MS et al (2023b) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator. Soft Comput 27:4987–5001. https://doi.org/10.1007/s00500-023-08026-x

    Article  Google Scholar 

  • Carayannis EG, Canestrino R, Magliocca P (2023) From the dark side of industry 4.0 to society 5.0: looking “beyond the box” to developing human-centric innovation ecosystems. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2023.3239552

    Article  Google Scholar 

  • Cheng B, Zhu D, Zhao S, Chen J (2016) Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Trans Netw Serv Manag 13(2):349–361

    Article  Google Scholar 

  • Cong P, Xiao Y, Wan X, Deng M, Li J, Zhang X (2023) DACR-AMTP: adaptive multi-modal vehicle trajectory prediction for dynamic drivable areas based on collision risk. IEEE Trans Intell Veh. https://doi.org/10.1109/TIV.2023.3321656

    Article  Google Scholar 

  • Di Vaio A, Hassan R, D’Amore G, Strologo AD (2022) Digital technologies for sustainable waste management on-board ships: an analysis of best practices from the cruise industry. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2022.3197241

    Article  Google Scholar 

  • Dou H, Liu Y, Chen S et al (2023) A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways. Soft Comput 27:16373–16388. https://doi.org/10.1007/s00500-023-09164-y

    Article  Google Scholar 

  • Fan W, Yang L, Bouguila N (2021) Unsupervised grouped axial data modeling via hierarchical Bayesian nonparametric models with Watson distributions. IEEE Trans Pattern Anal Mach Intell 44(12):9654–9668

    Article  Google Scholar 

  • Guo Y, Zhang C, Wang C, Jia X (2022) Towards public verifiable and forward-privacy encrypted search by using blockchain. IEEE Trans Depend Secure Compu. https://doi.org/10.1109/TDSC.2022.3173291

    Article  Google Scholar 

  • Huang W, Wang X, Zhang J, Xia J, Zhang X (2023) Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics. Food Control 145:109496

    Article  Google Scholar 

  • Jiang Z, Xu C (2023) Disrupting the technology innovation efficiency of manufacturing enterprises through digital technology promotion: an evidence of 5G technology construction in China. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2023.3261940

    Article  Google Scholar 

  • Karagiannopoulos PS, Manousakis NM, Psomopoulos CS (2023) “3R” practices focused on home appliances sector in terms of green consumerism: principles, technical dimensions and future challenges. IEEE Trans Consum Electron. https://doi.org/10.1109/TCE.2023.3318874

    Article  Google Scholar 

  • Li QK, Lin H, Tan X, Du S (2018) H∞ consensus for multiagent-based supply chain systems under switching topology and uncertain demands. IEEE Trans Syst Man Cybern Syst 50(12):4905–4918

    Article  Google Scholar 

  • Li J, Yang X, Shi V, Cai G (2023) Partial centralization in a durable-good supply chain. Prod Oper Manag 32:2775–2787

    Article  Google Scholar 

  • Liu X, Wang S, Lu S, Yin Z, Li X, Yin L, Tian J, Zheng W (2023) Adapting feature selection algorithms for the classification of Chinese texts. Systems 11(9):483

    Article  Google Scholar 

  • Lu S, Ding Y, Liu M, Yin Z, Yin L, Zheng W (2023a) Multiscale feature extraction and fusion of image and text in VQA. Int J Comput Intell Syst 16(1):54

    Article  Google Scholar 

  • Lu S, Liu M, Yin L, Yin Z, Liu X, Zheng W (2023b) The multi-modal fusion in visual question answering: a review of attention mechanisms. PeerJ Comput Sci 9:e1400

    Article  Google Scholar 

  • Ma K, Li Z, Liu P, Yang J, Geng Y, Yang B, Guan X (2021) Reliability-constrained throughput optimization of industrial wireless sensor networks with energy harvesting relay. IEEE Internet Things J 8(17):13343–13354

    Article  Google Scholar 

  • Mi C, Huang S, Zhang Y, Zhang Z, Postolache O (2022) Design and implementation of 3-D measurement method for container handling target. J Mar Sci Eng 10(12):1961

    Article  Google Scholar 

  • Mykytenko V (2023) Electro-optical surveillance systems for unmanned ground vehicle. Advanced system development technologies I. Springer, Cham, pp 49–83

    Google Scholar 

  • Samadhiya A, Agrawal R, Kumar A, Garza-Reyes JA (2023) Regenerating the logistics industry through the physical internet paradigm: a systematic literature review and future research orchestration. Comput Ind Eng 178:109150

    Article  Google Scholar 

  • Sharma R, Kamble S, Mani V, Belhadi A (2022) An empirical investigation of the influence of industry 4.0 technology capabilities on agriculture supply chain integration and sustainable performance. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2022.3192537

    Article  Google Scholar 

  • Taneja S, Siraj A, Ali L, Kumar A, Luthra S, Zhu Y (2023) Is fintech implementation a strategic step for sustainability in today’s changing landscape? An empirical investigation. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2023.3262742

    Article  Google Scholar 

  • Tuli S, Casale G, Jennings NR (2023) PreGAN+: semi-supervised fault prediction and preemptive migration in dynamic mobile edge environments. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2023.3330679

    Article  Google Scholar 

  • Wang L, Zhai Q, Yin B, et al (2019) Second-order convolutional network for crowd counting. In: Proceedings of SPIE 11198, fourth international workshop on pattern recognition, 111980T (31 July 2019). https://doi.org/10.1117/12.2540362

  • Wu W, Shen L, Zhao Z, Li M, Huang GQ (2022) Industrial IoT and long short-term memory network-enabled genetic indoor-tracking for factory logistics. IEEE Trans Ind Inform 18(11):7537–7548

    Article  Google Scholar 

  • Wu Q, Li X, Wang K et al (2023) Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles. Soft Comput 27:18195–18213. https://doi.org/10.1007/s00500-023-09278-3

    Article  Google Scholar 

  • Xu Y, Chen H, Wang Z, Yin J, Shen Q, Wang D, Huang F, Lai L, Zhuang T, Ge J, Hu X (2023a) Multi-factor sequential re-ranking with perception-aware diversification. arXiv preprint arXiv:2305.12420

  • Xu A, Qiu K, Zhu Y (2023b) The measurements and decomposition of innovation inequality: based on industry−university—research perspective. J Bus Res 157:113556

    Article  Google Scholar 

  • Xu H, Sun Z, Cao Y et al (2023c) A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Comput. https://doi.org/10.1007/s00500-023-09037-4

    Article  Google Scholar 

  • Yao W, Guo Y, Wu Y, Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese control conference (CCC). IEEE, pp 4192–4197. https://doi.org/10.23919/ChiCC.2017.8028015

  • Yin B, Khan J, Wang L, Zhang J, Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In: 2019 Chinese control conference (CCC). IEEE, pp 6772–6777. https://doi.org/10.23919/ChiCC.2019.8866334

  • You L, Danaf M, Zhao F, Guan J, Azevedo CL, Atasoy B, Ben-Akiva M (2023) A federated platform enabling a systematic collaboration among devices, data and functions for smart mobility. IEEE Trans Intell Transp Syst 24(4):4060–4074

    Article  Google Scholar 

  • Zong C, Wan Z (2022) Container ship cell guide accuracy check technology based on improved 3D point cloud instance segmentation. Brodogradnja: Teorija i Praksa Brodogradnje i Pomorske Tehnike 73(1):23–35

    Article  Google Scholar 

Download references

Acknowledgements

This paper acknowledges that it did not receive financial support or backing from any funding sources during the research, writing, or publication process.

Funding

No funding was provided for the completion of this study.

Author information

Authors and Affiliations

  1. Business School, Chongqing City Management College, Chongqing, 401331, China

    Xin Li

Authors
  1. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Li.

Ethics declarations

Conflict of interest

The author confirms the absence of any conflicting interests that could potentially influence the research outcomes or interpretations.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Li, X. Optimization of logistics flow management through big data analytics for sustainable development and environmental cycles. Soft Comput 28, 2701–2717 (2024). https://doi.org/10.1007/s00500-023-09591-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09591-x

Keywords

Search

Navigation