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

Abstract

Large-scale machinery operated in a coordinated 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 reinforcement learning with spatio-temporal safety constraints. The algorithm not only performed well on safety parameters, 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.

Graphical abstract

Keywords

deep reinforcement learning / mining earthwork / automated scheduling / spatio-temporal safety constraints

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