@inproceedings{18fca7173d3e49969006d396d3406b62,
title = "Research on Batch Scheduling Technology for Hybrid Flow Shop with Skip Ability Based on Improved Genetic Algorithm",
abstract = "Batch scheduling and dispatching in the hybrid flow shop production mode is an inevitable development trend, as it effectively improves low resource utilization and reduces excessive work-in-process inventory. This study proposes a multi-objective batch scheduling technique for the hybrid flow shop based on the concept of batch flow. It establishes a multi-objective optimization model with the sub-batch as the smallest processing unit and utilizes an improved genetic algorithm to enhance search capability, resolving the optimization issues of work-in-process inventory and delivery time in the hybrid flow shop. The approach is further developed and analyzed using actual data from a specific workshop, demonstrating its effectiveness in enhancing production efficiency and reducing work-in-process inventory. The results validate the feasibility and efficacy of the proposed algorithm.",
keywords = "Batch Flow Scheduling, Genetic Algorithm, Hybrid Flow Shop, Non-Dominated Sorting",
author = "Yunduo Wang and Aimin Wang and Yue Rong",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2023 ; Conference date: 15-09-2023 Through 17-09-2023",
year = "2023",
doi = "10.1109/AICIT59054.2023.10277705",
language = "English",
series = "2023 International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2023",
address = "United States",
}