运输
温室气体
燃料效率
卡车
调度(生产过程)
车队管理
工程类
有效载荷(计算)
强化学习
汽车工程
生产力
运筹学
环境科学
运输工程
计算机科学
运营管理
生物
算法
计算机网络
宏观经济学
人工智能
网络数据包
绳子
经济
生态学
作者
Da Huo,Yuksel Asli Sari,Ryan Kealey,Qian Zhang
标识
DOI:10.1016/j.resconrec.2022.106664
摘要
In typical mining operations, more than half of the direct greenhouse gas (GHG) emissions come from haulage fuel consumption. Smarter truck fleet dispatching is a feasible and manageable solution to reduce direct emissions with existing equipment. Conventional scheduling-based and human-led dispatching solutions often cause lower efficiency that wastes resources and elevates emissions. In this study, a simulated environment is developed to enable testing smarter real-time dispatching systems, Q-learning as a model-free reinforcement learning algorithm is used to improve fleet productivity, decrease waiting time and, consequently, reduce GHG emissions. The proposed algorithm trains the fleet to make better decisions based on payload, traffic, queueing, and maintenance conditions. Results show that this solution can reduce GHG emissions from haulage fuel consumption by over 30% while achieving the same production levels as compared to fixed scheduling. The proposed solution also shows advantages in handling operational randomness and balancing fleet size, productivity, and emissions.
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