| 基于Multi-agent技术的露天矿山生产调度系统的优化实现 |
 点此下载全文 |
| 引用本文：霍晓宇，杨仕教.基于Multi-agent技术的露天矿山生产调度系统的优化实现[J].计算技术与自动化,2013,(2):21-26 |
| 摘要点击次数: 1557 |
| 全文下载次数: 136 |
|
|
| 中文摘要:针对动态变化、复杂非线性的露天矿山生产调度系统引入多智能体技术进行建模优化，将系统分为任务Agent、生产调度Agent、爆破Agent、运输Agent以及破碎Agent五个单智能体。在任务Agent中给出矿山的矿石需求量，运用自适应神经模糊推理系统（ANFIS）和遗传算法（GA）对爆破Agent进行建模优化；破碎Agent根据破碎能力以及破碎需求量对运输Agent进行约束，引入自适应变异程序改进粒子群算法(PSO)对运输Agent进行优化；生产调度Agent协调处理矿石需求量与各生产工艺Agent生产能力之间的矛盾，运用MATLAB软件对模型进行模拟实现。以某露天矿山为例构建露天矿山生产调度系统，建模优化结果表明该方法可行。 |
| 中文关键词:露天矿山生产调度系统 多智能体 GA-ANFIS算法 粒子群优化算法 |
| |
| Optimized Implementation of Open-pit Mine Production Scheduling System Based on Multi-agent Technology |
|
|
| Abstract:For the dynamic changes and complex nonlinear of open-pit mine production scheduling system, the multi-agent technology was introduced to carry out optimized models. In the model, the system was divided into 5 single-agents: Task-agent, Production Scheduling agent, Blasting-agent, Transport-agent and Crushing-agent. The demand for mine’s ore was given in Task-agent. Adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) was used to make modeling on Blasting-agent. According to crushing capacity and crushing demand, Transport-agent was constrained by Crushing-agent. Transport-agent was optimized by introducing adaptive mutation process to improve particle swarm optimization (PSO). Production scheduling agent was used to coordinate and handle thecontradiction of ore demand and production capability, and it utilized MATLAB to simulate models. An open-pit mine production scheduling system was based on a open-pit mine, the results of modeling optimization showed that the method was feasible. |
| keywords:open-pit mine production scheduling system multi-agent GA-ANFIS particle swarm optimization |
| 查看全文 查看/发表评论 下载pdf阅读器 |
位访问者,今日访问
