Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14969
Title: Supply Chain Schedule Management with Genetic Algorithm
Authors: Yadav, Puneet Kumar
Singh, Shalini
Tripathi, Chandan Mani
Bhushan, Ravi
Keywords: Improved Genetic Algorithm
Logistics and Transportation
Scheduling Modelling
Supply Chain Logistics
Issue Date: 2023
Publisher: 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023
Institute of Electrical and Electronics Engineers Inc.
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.
URI: https://doi.org/10.1109/EASCT59475.2023.10393211
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14969
ISBN: 9.79835E+12
Appears in Collections:Conference Papers

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