Bibliographic Details
| Title: |
Optimization Model for Mine Backfill Scheduling Under Multi-Resource Constraints. |
| Authors: |
Liu, Yuhang1,2 (AUTHOR), Li, Guoqing1 (AUTHOR) qqlee@ustb.edu.cn, Hou, Jie1 (AUTHOR), Fan, Chunchao1,3 (AUTHOR), Tong, Chuan1,3 (AUTHOR), Wang, Panzhi3 (AUTHOR) |
| Source: |
Minerals (2075-163X). Dec2024, Vol. 14 Issue 12, p1183. 21p. |
| Subject Terms: |
*DECODING algorithms, *HEURISTIC algorithms, *GENETIC algorithms, *DATA mining, *LABOR supply |
| Abstract: |
Addressing the resource constraints, such as manpower and equipment, faced by mine backfilling operations, this study proposed an optimization model for backfill scheduling based on the Resource-Constrained Project Scheduling Problem (RCPSP). The model considered backfilling's multi-process, multi-task, and multi-resource characteristics, aiming to minimize total delay time. Constraints included operational limits, resource requirements, and availability. The goal was to determine optimal resource configurations for each stope's backfilling steps. A heuristic genetic algorithm (GA) was employed for solution. To handle equipment unavailability, a new encoding/decoding algorithm ensured resource availability and continuous operations. Case verification using real mine data highlights the advantages of the model, showing a 20.6% decrease in completion time, an 8 percentage point improvement in resource utilization, and a 47.4% reduction in overall backfilling delay time compared to traditional methods. This work provides a reference for backfilling scheduling in similar mines and promotes intelligent mining practices. [ABSTRACT FROM AUTHOR] |
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