Please use this identifier to cite or link to this item:
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14969
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yadav, Puneet Kumar | - |
dc.contributor.author | Singh, Shalini | - |
dc.contributor.author | Tripathi, Chandan Mani | - |
dc.contributor.author | Bhushan, Ravi | - |
dc.date.accessioned | 2024-03-30T10:11:00Z | - |
dc.date.available | 2024-03-30T10:11:00Z | - |
dc.date.issued | 2023 | - |
dc.identifier.isbn | 9.79835E+12 | - |
dc.identifier.uri | https://doi.org/10.1109/EASCT59475.2023.10393211 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14969 | - |
dc.description.abstract | In a modern logistics system, it is a very important issue how to organize transportation and 'distribution' in a reasonable way in order to reduce the cost of 'storage'. Transportation is the most crucial part of the modern logistics subsystem. Transport plays an important role in the development of our country, for example by expanding the scope of distribution, expanding the tangibility of markets and promoting the division of labour in society. The aim of this work is to study the modelling of supply chain logistics and transport scheduling based on an improved genetic algorithm. Based on the study of this method, it is improved and compared with the control parameters of the genetic algorithm. Next, the supply chain logistics and transportation scheduling problem studied in this paper is described, the assumptions and definitions of the supply chain scheduling model are explained, the process of constructing the supply chain model is analysed and it is solved using the improved genetic algorithm. Experimental results show that this algorithm is able to bypass the shortcomings of traditional genetic algorithms and thus obtain satisfactory global optima. © 2023 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.publisher | 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023 | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.subject | Improved Genetic Algorithm | en_US |
dc.subject | Logistics and Transportation | en_US |
dc.subject | Scheduling Modelling | en_US |
dc.subject | Supply Chain Logistics | en_US |
dc.title | Supply Chain Schedule Management with Genetic Algorithm | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.