Multi-Objective Scheduling Strategy of Mine Transportation Robot Based on Three-Dimensional Loading Constraint

• Published in: Minerals (Basel) Vol. 13; no. 3; p. 431
• Main Authors: Yan, Xuanxuan, Wang, Guorong, Jiang, Kuosheng, Kou, Ziming, Wang, Kaisong, Zhang, Lixiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Adaptability; Algorithms; Analysis; Cluster analysis; Clustering; Coal; Coal industry; Coal mines; Coal mining; Containers; Cost control; Delay time; Distribution; Efficiency; Genetic algorithms; Heuristic; Heuristic methods; Inventory; Mathematical models; Mineral industry; Mines; Mining industry; Multiple objective analysis; Occupational health and safety; Optimization; Optimization models; Pareto optimum; Planning; R&D; Research & development; Robots; Scheduling; Three dimensional models; Transportation industry; Transportation systems; Underground mines; Vehicles
• ISSN: 2075-163X; 2075-163X
• DOI: 10.3390/min13030431

In an attempt to solve the problems of the low intelligent distribution degree and high working intensity of auxiliary transportation systems in underground coal mines, an intelligent distribution strategy of materials in the whole mine is put forward. Firstly, combined with the characteristics of materials and standard containers, a three-dimensional loading model is established with the goal of maximizing the space utilization of standard containers, and a three-dimensional space segmentation heuristic algorithm is used to solve the material loading scheme. Then, the multi-objective optimization model of distribution parameters is established with the goal of the shortest delivery distance, the shortest delay time, and the fewest number of delivery vehicles, and the dual-layer genetic algorithm is used to solve the distribution scheme. Finally, the spatiotemporal conversion coefficient is designed to solve the task list by hierarchical clustering, and the solution time is reduced by 30%. The results show that the dual-layer genetic algorithm based on hierarchical clustering has good adaptability in complex material scheduling scenarios.