A reinforcement learning based dynamic multi-objective constrained evolutionary algorithm for open-pit mine truck scheduling

Date

2023-09

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods.

Description

Keywords

Truck scheduling, real-time ore blending, search operator selection, DCNSGA-III

Citation

Qiu, J., Li, C. and Yang, S. (2023) A reinforcement learning based dynamic multi-objective constrained evolutionary algorithm for open-pit mine truck scheduling. Proceedings of the 2023 China Automation Congress.

Rights

Attribution 2.0 UK: England & Wales
http://creativecommons.org/licenses/by/2.0/uk/

Research Institute

Institute of Artificial Intelligence (IAI)