Deep Reinforcement Learning for automated scheduling of mining earthwork equipment with spatio-temporal safety constraints  

作　　者：Yanan LU Ke YOU Yuxiang WANG Ying LIU Cheng ZHOU Yutian JIANG Zhangang WU 

机构地区：[1]National Center of Technology Innovation for Digital Construction,Huazhong University of Science and Technology,Wuhan 430074,China [2]School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China [3]Institute of Artificial Intelligence,Huazhong University of Science and Technology,Wuhan 430074,China [4]Shantui Construction Machinery Co.,Ltd,Jining 272000,China

出　　处：《Frontiers of Engineering Management》2025年第1期39-58,共20页工程管理前沿(英文版)

基　　金：National Natural Science Foundation of China(Grant Nos.72171092,52192664 and 71821001);Natural Science Fund for Distinguished Young Scholars of Hubei Province,China(Grant No.2021CFA091).

摘　　要：Large-scale machinery operated in a coordinat-ed manner in earthworks for mining constitutes high safety risks.Efficient scheduling of such machinery,factoring in safety constraints,could save time and significantly improve the overall safety.This paper develops a model of automated equipment scheduling in mining earthworks and presents a scheduling algorithm based on deep rein-forcement learning with spatio-temporal safety constraints.The algorithm not only performed well on safety parame-ters,but also outperformed randomized instances of various sizes set against real mining applications.Further,the study reveals that responsiveness to spatio-temporal safety constraints noticeably increases as the scheduling size increases.This method provides important noticeable improvements to safe automated scheduling in mining.

关 键 词：deep reinforcement learning mining earthwork automated scheduling spatio-temporal safety constraints 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]