Enhanced green logistics: sustainable distribution and warehousing with IMU positioning

• Autorzy: Wu Xiangfeng

Identyfikatory

DOI

10.2478/eces-2024-0016

• Języki publikacji: EN

Abstrakty

EN

The objective of the green logistics distribution model is to minimise environmental pollution and energy usage by employing clean energy, optimising transport routes and enhancing transport efficiency. Nonetheless, current studies on green logistics distribution models and warehousing planning exhibit certain drawback, such as imprecise location accuracy and decreased distribution revenues. To overcome these challenges, this paper proposes a novel approach that combines inertial measurement unit (IMU) and binocular vision, leveraging multisource information positioning. Specifically, the method integrates data collection and preprocessing modules to gather crucial logistics distribution task information, encompassing IMU data, image data and vehicle data. The visual and inertial positioning module consists of two components: visual positioning based on the grey centre method and IMU positioning based on the integral essence. Finally, an adaptive Kalman filter is employed to merge the results of visual positioning and IMU positioning, thus producing the ultimate logistics vehicle positioning result. The proposed method effectively addresses existing challenges in the green logistics distribution model and warehouse planning. In particular, the experimental results demonstrate that the algorithm proposed in this study reduces the location error by 8%. Furthermore, logistics and distribution costs are reduced by 11 %, contributing to the promotion of sustainable and environmentally friendly logistics operations.

Słowa kluczowe

EN

green logistics distribution; inertial measurement unit; IMU; visuality; adaptive Kalman filtering

PL

dystrybucja; zielona logistyka; inercyjny układ pomiarowy; wizualność; adaptacyjny filtr Kalmana

• Wydawca: Towarzystwo Chemii i Inżynierii Ekologicznej
• Czasopismo: Ecological Chemistry and Engineering. S
• Rocznik: 2024
• Tom: Vol. 31, nr 2
• Strony: 225--241
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Wu Xiangfeng

• School of Management, Shandong Polytechnic, Jinan 250104, Shandong, China

• https://orcid.org/0009-0001-8524-9299

Bibliografia

• [1] Konstantakopoulos GD, Gayialis SP, Kechagias EP. Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification. Operational Res. 2022;22:2033-62. DOI: 10.1007/s12351-020-00600-7.
• [2] Amling A, Daugherty PJ. Logistics and distribution innovation in China. Int J Physical Distribution Logistics Manage. 2018;50(3):323-32. DOI: 10.1108/IJPDLM-07-2018-0273.
• [3] Song Y, Yu FR, Zhou L, Yang X, He Z. Applications of the Internet of Things (IoT) in smart logistics: A comprehensive survey. IEEE Internet of Things J. 2020;8(6):4250-74. DOI: 10.1109/JIOT.2020.3034385.
• [4] Melkonyan A, Gruchmann T, Lohmar F, Kamath V, Spinler S. Sustainability assessment of last-mile logistics and distribution strategies: The case of local food networks. Int J Production Economics. 2020;228:107746. DOI: 10.1016/j.ijpe.2020.107746.
• [5] Van Goor AR, van Amstel WP, van Amstel MP. Europ Distribution and Supply Chain Logistics. Routledge. 2019. DOI: 10.4324/9781003021841.
• [6] Paciarotti C, Torregiani F. The logistics of the short food supply chain: A literature review. Sust Prod Consumption. 2021;26:428-42. DOI: 10.1016/j.spc.2020.10.002.
• [7] Al Theeb N, Smadi HJ, Al-Hawari TH, Aljarrah MH. Optimisation of vehicle routing with inventory allocation problems in Cold Supply Chain Logistics. Computers Industrial Eng. 2020;142:106341. DOI: 10.1016/j.cie.2020.106341.
• [8] Wu CH, Tsai SB, Liu W, Shao XF, Xia YK, Wacławek M. Green environment and sustainable development: methods and applications. Ecol Chem Eng S. 2021;28(4):467-70. DOI: 10.2478/eces-2021-0030.
• [9] Liu W, Tsai SB, Wu CH, Shao X, Wacławek M. Corporate environmental management and sustainable operation: theory and application. Ecol Chem Eng S. 2022;29(3):283-5. DOI: 10.2478/eces-2022-0020.
• [10] Li T, Donta PK. Predicting green supply chain impact with SNN-stacking model in digital transformation context. J Organizational End User Computing (JOEUC). 2023;35(1):1-19. DOI: 10.4018/JOEUC.334109.
• [11] Golpîra H, Khan SAR, Safaeipour S. A review of logistics internet-of-things: Current trends and scope for future research. J Industrial Informat Integration. 2021;22:100194. DOI: 10.1016/j.jii.2020.100194.
• [12] Ding Y, Jin M, Li S, Feng D. Smart logistics based on the internet of things technology: an overview. Int J Logistics Res Appl. 2021;24(4):323-45. DOI: 10.1080/13675567.2020.1757053.
• [13] Wu CH. An empirical study on selection, evaluation, and management strategies of green suppliers in manufacturing enterprises. J Organizational End User Computing (JOEUC). 2022;34(1):1-18. DOI: 10.4018/JOEUC.307568.
• [14] Pan W, Liu SQ. Deep reinforcement learning for the dynamic and uncertain vehicle routing problem. Appl Intelligence. 2022;53(1):405-22. DOI: 10.1007/s10489-022-03456-w.
• [15] Lei S, Chen C, Li Y, Hou Y. Resilient disaster recovery logistics of distribution systems: Co-optimise service restoration with repair crew and mobile power source dispatch. IEEE Trans Smart Grid. 2018;10(6):6187-202. DOI: 10.1109/TSG.2019.2899353.
• [16] Lemonte AJ. The beta log-logistic distribution. Braz J Probab Stat. 2014;28(3):313-32. DOI: 10.1214/12-BJPS209.
• [17] Saber MM, Yousof H. Bayesian and classical inference for generalised stress-strength parameter under generalised logistic distribution. Statistics. Optimisation Information Computing. 2023;11(3):541-53. DOI: 10.19139/soic-2310-5070-1292.
• [18] Rushton A, Croucher P, Baker P. The Handbook of Logistics and Distribution Management: Understanding the Supply Chain. Kogan Page Publishers. 2010. DOI: 10.1108/ijppm.2005.07954bae.001.
• [19] Zhang Y, Kou X, Song Z, Fan Y, Usman M, Jagota V. Research on logistics management layout optimisation and real-time application based on nonlinear programming. Nonlinear Eng. 2022;10(1):526-34. DOI: 10.1515/nleng-2021-0043.
• [20] Alshurideh M, Alquqa E, Alzoubi H, Alkurdi B, Hamadneh S. The effect of information security on e-supply chain in the UAE logistics and distribution industry. Uncertain Supply Chain Manage. 2023;11(1):145-52. DOI: 10.5267/j.uscm.2022.11.001.
• [21] Ramadan AT, Tolba AH, El-Desouky BS. A unit half-logistic geometric distribution and its application in insurance. Axioms. 2022;11(12): 676. DOI: 10.3390/axioms11120676.
• [22] Yang M, Mahmood M, Zhou X, Shafaq S, Zahid L. Design and implementation of cloud platform for intelligent logistics in the trend of intellectualization. China Commun. 2017;14(10):180-91. DOI: 10.1109/CC.2017.8107642.
• [23] Nuanmeesri S. Mobile application for the purpose of marketing, product distribution and location-based logistics for elderly farmers. Appl Computing Informatics. 2023;19(1/2):2-21. DOI: 10.1016/j.aci.2019.11.001.
• [24] Bai Q, Yin X, Lim MK, Dong C. Low-carbon VRP for cold chain logistics considering real-time traffic conditions in the road network. Ind Manage Data Systems. 2022;122(2):521-43. DOI: 10.1108/IMDS-06-2020-0345.
• [25] Sun Z, Wang Q, Chen L, Hu C. Unmanned technology-based civil-military intelligent logistics system: from construction to integration. J Beijing Inst Technol. 2022;31(2):140-51. DOI: 10.15918/j.jbit1004-0579.2022.010.
• [26] Gayialis SP, Kechagias EP, Konstantakopoulos GD. A city logistics system for freight transportation: Integrating information technology and operational research. Operational Res. 2022;22(5):5953-82. DOI: 10.1007/s12351-022-00695-0.
• [27] Fernie J, Sparks L, McKinnon AC. Retail logistics in the UK: past, present and future. Int J Retail Distribution Manage. 2010;38(11/12):894-914. DOI: 10.1108/09590551011085975.
• [28] Mishra S, Singh SP. Designing dynamic reverse logistics network for post-sale service. Annals Operations Res. 2022:1-30. DOI: 10.1007/s10479-020-03710-9.
• [29] Keshavarz-Ghorabaee M. Assessment of distribution center locations using a multi-expert subjective-objective decision-making approach. Sci Rep. 2021;11:19461. DOI: 10.1038/s41598-021-98698-y.
• [30] Cai W, Song Y, Duan H, Xia Z, Wei Z. Multi-feature fusion-guided multiscale bidirectional attention networks for logistics pallet segmentation. Computer Modeling Eng Sci. 2022;131(3):1539-55. DOI: 10.32604/cmes.2022.019785.