Open AccessIntelligent Scheduling System for Production Line Automatic Matching Based on DSSM-XGBoost
Author(s) -
Shuaihu Yang,
Ming Feng,
Danping Guan
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2203/1/012072
Subject(s) - computer science , scheduling (production processes) , production line , industrial engineering , real time computing , mathematical optimization , engineering , mathematics , mechanical engineering
Intelligent production scheduling is an important part of intelligent production, and its production time and intensity are reasonably arranged, which can fully improve production efficiency. This article provides an intelligent model for engine manufacturing and scheduling. After the model decomposes the order into processes, the DSSM algorithm solves the semantic similarity of the text in the production content of the process, and generates a semantic vector to convert unstructured data into structured data. Based on XGBoost algorithm, the required production line is marked to establish the association between the production content and the production line, and realize automatic matching. After classification, the average working hours and other information are estimated according to the established logic. Finally, schedule production based on the principle of maximum utilization of the production line. Through testing and evaluation, the scheduling results of this smart scheduling model can be completed in just a few seconds, which saves time and costs in the scheduling process.