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Data-driven Hierarchical Learning and Real-time Decision-making of Equipment Scheduling and Location Assignment in Automatic High-density Storage Systems by Zhun Xu; Liyun Xu; Xufeng Ling; Beikun Zhang is a Engineering article available to read on EtoBox.

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Automated high-density storage systems (AHDSS) have attracted widespread attention in recent years owing to their advantages of high throughput and space utilisation. However, owing to the characteristics of large-scale, multi-disturbance, and short-period task scenarios, a system is required to make instant and efficient decisions. To this end, this paper proposes a data-driven real-time decision-making method to solve the real-time equipment scheduling and dynamic location assignment problem in AHDSS. The proposed method comprises two phases: decision scheme learning and real-time decision-making. The operation state attribute features of the AHDSS were constructed to generate training data for equipment scheduling and location assignment scheme learning. Thereafter, a hierarchical learning and decision-making mechanism based on the deep belief network (DBN) is proposed. The integrated learning of better scheduling solutions was realised by establishing three-stage models of lift selection, shuttle selection, and location priority. Additionally, the Taguchi method was adopted to determine the best performance parameters for DBNs at different learning stages. Compared with other w

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Author

Zhun Xu; Liyun Xu; Xufeng Ling; Beikun Zhang

Publisher

Informa UK Limited

Published

2022

Language

EN

Field

Engineering (Physical Sciences)