Open AccessMulti-task Scheduling of Agvs System Based on Improved NSGA-II Algorithm
Author(s) -
Ren Ting-ge,
Yueyang Ren
Publication year - 2021
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 - 10.1088/1742-6596/1924/1/012007
Subject(s) - crossover , computer science , task (project management) , energy consumption , algorithm , mathematical optimization , scheduling (production processes) , population , stability (learning theory) , engineering , mathematics , artificial intelligence , machine learning , demography , electrical engineering , systems engineering , sociology
Aiming at the problem that the AGV energy consumption is not considered in the multi-task mode, a multi-task model is established which takes the total energy consumption of AGV and the total time of tasks out of warehouse as the goal, and the task group reconstruction is realized based on the batch-combination strategy. Then, this paper improves the NSGA-II algorithm to solve the model from the following three aspects: population screening mechanism, pheromone-based crossover, and double mutation operate. The experimental simulation results show that the improved NSGA-II algorithm improves the quality of the solutions, and meanwhile improves the stability of the algorithm compared with theoriginal NSGA-II algorithm.