THE IMPORTANTS OF CLUSTERING IN LOGISTIC SYSTEMS
DOI:
https://doi.org/10.20998/2078-7405.2021.94.02Keywords:
Industry 4.0, clustering, heuristics, decision-making, routingAbstract
Nowadays, the development of higher efficient processes and procedures is the key for success in industrial environment. The companies have machines, production lines, software and hardware tools with high level principles of efficient working. Example: the Industry 4.0 concept use the machines and methods of the near past, upgrade them, and gave them new purpose, as a more efficient tool. Some of the bases of those tools are not as efficient as which many would think, like in group generating or in other word clustering. Clustering is a very hard process, and it is in almost every decision making in every company’s lives. It is important to sometimes examine its significance and flaws. This paper presents the clustering briefly and shows its errors through an example.
References
Bányai, T.: Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review, INDUSTRY 4.0 - IMPACT ON INTELLIGENT LOGISTICS AND MANUFACTURING, pp 1-9, 2020; https://doi.org/10.5772%2Fintechopen.89387
Dobos, P., Tamás, P., Illés, B., Balogh, R.: Application possibilities of the Big Data concept in Industry 4.0, IOP CONFERENCE SERIES: MATERIALS SCIENCE AND ENGINEERING 448: 1 Paper: 012011, 2018
Illés, B., Tamás, P., Dobos, P., Skapinyecz, R.: New challenges for quality assurance of manufacturing processes in industry 4.0, Solid State Phenomena. Vol. 261. Trans Tech Publications Ltd, 2017; https://doi.org/10.4028/www.scientific.net/SSP.261.481
Borodavko, B., Illés, B., Bányai, Á. Role of artificial intelligence in supply chain, ACADEMIC JOURNAL OF MANUFACTURING ENGINEERING 19: 1 pp. 75-79. , 2021
Timothy, R.: Google's DeepMind AI discovers physics, NEW SCIENTIST, Volume 232, Issue 3100, Page 25, 2016; https://doi.org/10.1016/S0262-4079(16)32121-2
Saraswati J., Brian F., Rafiq A.: Lean OR ERP – A Decision Support System to Satisfy Business Objectives, PROCEDIA CIRP Volume 70, Pages 422-427, 2018, https://doi.org/10.1016/j.procir.2018.02.048
Bryar A.H., Tarik A. R., Seyedali M.: Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets, DATA IN BRIEF Volume 36, 2021, p. 107044, 2021
Zun-yang L., Feng D., YingX.X.H.: Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm, DEFENCE TECHNOLOGY 2020. https://doi.org/10.1016/j.dt.2020.10.002
Muthukrishnan: Mathematics behind K-Mean Clustering algorithm ARTIFICIAL INTELLIGENCE DATA SCIENCE, 2018
Tamás P., Illés B., Bányai T., Bányainé T. Á., Umetaliev A., Skapinyecz R.: Intensifying Cross-border Logistics Collaboration Opportunities Using a Virtual Logistics Center, JOURNAL OF ENGINEERING RESEARCH AND REPORTS 13, Paper :3, 2020
Downloads
Published
Issue
Section
License
Copyright Notice
Authors who publish with this Collection agree to the following terms:
1. Authors retain copyright and grant the Collection right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this Collection.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the Collection's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this Collection.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
