Version 1
: Received: 14 June 2023 / Approved: 15 June 2023 / Online: 15 June 2023 (12:10:58 CEST)
How to cite:
Wang, L.; Liu, H.; Xia, M.; Wang, Y.; Li, M. A Machine Learning Based EMA-DCPM Algorithm for Production Scheduling. Preprints2023, 2023061138. https://doi.org/10.20944/preprints202306.1138.v1
Wang, L.; Liu, H.; Xia, M.; Wang, Y.; Li, M. A Machine Learning Based EMA-DCPM Algorithm for Production Scheduling. Preprints 2023, 2023061138. https://doi.org/10.20944/preprints202306.1138.v1
Wang, L.; Liu, H.; Xia, M.; Wang, Y.; Li, M. A Machine Learning Based EMA-DCPM Algorithm for Production Scheduling. Preprints2023, 2023061138. https://doi.org/10.20944/preprints202306.1138.v1
APA Style
Wang, L., Liu, H., Xia, M., Wang, Y., & Li, M. (2023). A Machine Learning Based EMA-DCPM Algorithm for Production Scheduling. Preprints. https://doi.org/10.20944/preprints202306.1138.v1
Chicago/Turabian Style
Wang, L., Yu Wang and Mingfei Li. 2023 "A Machine Learning Based EMA-DCPM Algorithm for Production Scheduling" Preprints. https://doi.org/10.20944/preprints202306.1138.v1
Abstract
Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.
Keywords
Mixed production lines; Multiple varieties; Variable batches; EMA-DCPM; Production scheduling
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
