@inproceedings{c000eaa29590442da1175bbf0ba0e54e,
title = "A Batch Scheduling Technique of Flexible Job-Shop Based on Improved Genetic Algorithm",
abstract = "In order to solve the flexible job-shop batch scheduling problem, based on the heuristic scheduling method, this paper design a flexible job shop batch scheduling algorithm, by using the framework of genetic algorithm and a flexible batch coding method based on 'cursors'. While finding the optimal batch strategy through genetic algorithm, the algorithm uses heuristic method to solve the sub-problem of sub-lot scheduling, taking into account the completeness and rapidity of solution. Finally, through simulation experiments, the results are analyzed to verify the effectiveness of the algorithm.",
keywords = "batch scheduling, flexible job-shop, genetic algorithm, heuristic",
author = "Yueshu Li and Aimin Wang and Shengwei Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Conference on Mechatronics and Automation, ICMA 2022 ; Conference date: 07-08-2022 Through 10-08-2022",
year = "2022",
doi = "10.1109/ICMA54519.2022.9856332",
language = "English",
series = "2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1463--1467",
booktitle = "2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022",
address = "United States",
}