Document Type : Original Research Paper

Authors

• Vahid Nikbin ¹
• Keyvan Saeedi ²
• Mohammad Hosein Salimi ³

¹ Mining group, Faculty of Engineering, University of Zanjan, Iran

² Mining group, Faculty of Engineering, University of Zanjan, Iran.

³ MSc Graduated, Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Loading and drilling fleets in open pit mines are increasingly dominated by crawler mounted equipment. Due to limited mobility, relocation takes longer, consumes more fuel and increases downtime. While existing fleet management literature predominantly concentrates on wheeled haulage fleets (truck-shovel dispatching), the integrated, short-term scheduling and allocation optimization of crawler-mounted drilling and loading equipment has received limited attention and remains reliant on heuristic rules or expert judgment. To address this challenge, a binary integer programming framework was developed to minimize allocation time for crawler mounted drill wagons and excavators across multiple mining faces in short term schedules. The framework incorporates practical operational rules, including constraints on operation scheduling, operation durations, drilling-to-loading precedence and blasting interval, movement continuity, equipment presence and transfer, and operation completion. Application to Zarshouran open pit gold mine over a three week horizon integrates inter face distance matrices, equipment speeds, blasting windows, and face specific drilling and loading times. Implementation in GAMS generates weekly equipment allocations, while validation against expert judgment confirms strong alignment with field decisions and highlights opportunities to enhance consistency and resource utilization. Mathematically, the formulation introduces a unified binary integer programming structure that simultaneously synchronizes heterogeneous drilling and loading fleets under strict inter-activity precedence, endogenous road-network allocation times, and spatial-temporal continuity constraints, distinguishing it from decoupled scheduling models. Results indicate that the model achieves improvements of 18.8%, 13.2%, and 33.7% in reducing the allocation time of crawler mounted equipment in Weeks 1 to 3, respectively, compared with allocations based on expert engineering judgment.

Keywords

• Equipment scheduling and allocation
• Crawler-mounted equipment
• Optimization
• Binary integer programming
• Short-term planning

Main Subjects

• Loading & Transportation