.png){kind=icon}
返回
A self-adaptive memetic algorithm with Q-learning for solving the multi-AGVs dispatching problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2024.101697.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we address the problem of dispatching multiple automated guided vehicles (AGVs) in an actual production workshop, aiming to minimize the transportation cost. To solve this problem, a self-adaptive memetic algorithm with Q-learning (Q-SAMA) is proposed. An improved nearest-neighbor task division heuristic is used for generating premium solutions. Additionally, a Q-learning is integrated to select appropriate neighborhood operators, thereby enhancing the algorithm's exploration ability. To prevent the algorithm from falling into a local optimum, the restart strategy is offered. In order to adapt Q-SAMA to different stages in the search process, the traditional crossover and mutation probabilities are no longer used. Instead, a self-adaptive probability is obtained based on the population's degree of concentration, and the sparsity relationship among individuals' fitness. Finally, experimental results validate the effectiveness of the proposed method. It is able to yield better results compared with other five state-of-the-art algorithms.
Keyword:
Automatic guided vehicle
Scheduling problem
Memetic algorithm
Q-learning
Task assignment
Self-adaptive methods
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
.png){kind=icon}
Invasive weed optimization for multi-AGVs dispatching problem in a matrix manufacturing workshop
PREAI
分享收藏A Generic Markov Decision Process Model and Reinforcement Learning Method for Scheduling Agile Earth Observation Satellites调度敏捷对地观测卫星的通用马尔可夫决策过程模型和强化学习方法
PREAI
分享收藏
查看更多内容