Education, Science, Technology, Innovation and Life
Open Access
Sign In

Machine Learning Algorithms in the Application of the Logistics Curriculum Reform

Download as PDF

DOI: 10.23977/curtm.2022.051310 | Downloads: 13 | Views: 395

Author(s)

Haimin Li 1

Affiliation(s)

1 Shandong Transport Vocational College, Weifang 261206, Shandong, China

Corresponding Author

Haimin Li

ABSTRACT

With the vigorous development of e-commerce, the status of China's logistics industry has become increasingly prominent, and the training of logistics professionals has gradually been concerned. The teaching purpose of secondary vocational education is mainly to cultivate front-line workers with strong practical ability and fast learning ability. Therefore, training logistics talents to meet the social needs has become the fundamental task of school logistics professional education. This paper discusses the application of machine learning algorithm(MLA) in the logistics distribution(LD) curriculum reform(CR), and analyzes the principles, key points and the principles of curriculum content screening of LD CR; This paper analyzes the specific use of MLA, and then discusses the organization and implementation of the curriculum content of logistics management specialty in secondary vocational schools based on MLA. Finally, through experimental analysis, it verifies that MLA has great significance for the success of LD CR.

KEYWORDS

Machine Learning Algorithm, Logistics Distribution, Curriculum Reform, Algorithm Application

CITE THIS PAPER

Haimin Li, Machine Learning Algorithms in the Application of the Logistics Curriculum Reform. Curriculum and Teaching Methodology (2022) Vol. 5: 53-60. DOI: http://dx.doi.org/10.23977/curtm.2022.051310.

REFERENCES

[1] Haager J B. "Sex Education's Many Sides": Eugenics and Sex Education in New York City's Progressive Reform Organizations [J]. The Journal of the Gilded Age and Progressive Era, 2022, 21(2):74-92.
[2] Yang S. Optimization of Urban Logistics Distribution path under dynamic Traffic Network [J]. International Core Journal of Engineering, 2020, 6(1):243-248.
[3] Haag L, Sandberg E, Sallns U. Towards an increased understanding of learning: a case study of a collaborative relationship between a retailer and a logistics service provider [J]. International Journal of Retail & Distribution Management, 2021, 50(13):44-58.
[4] Sun F, Shi G. Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm [J]. Journal of Enterprise Information Management, 2022, 35(4/5):1168-1184.
[5] Mangiaracina R, Seghezzi A, Siragusa C. Enhancing in-store picking for e-grocery: an empirical-based model [J]. International Journal of Physical Distribution & Logistics Management, 2022, 52(4):301-323.
[6] Ng M C, Farhani G, Farhani N. P.019 A machine learning approach to asymmetric burst suppression and refractory status epilepticus outcome[J]. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques, 2021, 48(s3):S25-S25.
[7] Srijamdee K, Charoenrat T, Kwanyou A, et al. Service marketing strategies and performances of tourism and hospitality enterprises: implications from a small border province in Thailand [J]. Asia Pacific Journal of Marketing and Logistics, 2022, 34(5):887-905.
[8] Zhou G, Huang J, Yu S X. Buy domestic or foreign brands? The moderating roles of decision focus and product quality [J]. Asia Pacific Journal of Marketing and Logistics, 2022, 34(4):843-861.
[9] Jain N K, Kaul D, Sanyal P. What drives customers towards mobile shopping? An integrative technology continuance theory perspective [J]. Asia Pacific Journal of Marketing and Logistics, 2022, 34(5):922-943.
[10] Lafkihi M, Kong X, Wang C, et al. The impact of gamification on teaching and learning Physical?Internet: a quasi-experimental study[J]. Industrial Management & Data Systems, 2022, 122(6):1499-1521.
[11] Qin Y, Yi Z, Zhang Y, et al. A study on the impact of digital tobacco logistics on tobacco supply chain performance: taking the tobacco industry in Guangxi as an example [J]. Industrial Management & Data Systems, 2022, 122(6):1416-1452.
[12] Trkman P, Tomat L, Manfreda A. Personality in information systems professions: identifying archetypal professions with suitable traits and candidates' ability to fake-good these traits [J]. Information Technology & People, 2021, 35(8):52-73.

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.