Simulation analysis of intelligent scheduling model of large mechanical work task
- 10.2991/amcce-15.2015.229How to use a DOI?
- large mechanical work task; optimal standard; scheduling conflict; intelligent scheduling; local particle optimization theory
As the complexity of the task of large mechanical work continues to increase, during the assembly scheduling process between each sub task, direct optimal standard of local scheduling have conflicts, contradiction of different sub task direct scheduling process is becoming more and more prominent. When the traditional intelligent scheduling method faces complicated mechanical work, is easy to be affected by scheduling contradiction between many local jobs, which makes the scheduling method fall in convergence, it is difficult to form an optimal scheduling scheme to meet the demand of all scheduling. This paper presents intelligent scheduling method for tasks of large mechanical work based on adding particle planning. The local particle optimization theory is introduced in the process of scheduling, the sub scheduling process is regarded as set to obtain the best solution for the order in the collection. Assigning intelligent scheduling process for tasks of large mechanical work reasonably, the most reasonable scheduling method is selected to realize optimal planning of task scheduling. The experimental results show that, algorithm used for assembly sequence planning in complex processes, can effectively avoid the conflict between the scheduling processes, and improve the efficiency of assembly execution.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xin-yan Rong AU - Zeng-xin Li AU - Li-yan Zhang PY - 2015/04 DA - 2015/04 TI - Simulation analysis of intelligent scheduling model of large mechanical work task BT - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/amcce-15.2015.229 DO - 10.2991/amcce-15.2015.229 ID - Rong2015/04 ER -