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OpenMines: A Light and Comprehensive Mining Simulation Environment for Truck Dispatching

Shi Meng, Bin Tian, Xiaotong Zhang, Shuangying Qi, Caiji Zhang, Qiang Zhang

TL;DR

This paper tackles the lack of open, reproducible environments for evaluating mine fleet dispatch algorithms by introducing OpenMines, a modular discrete-event simulation framework built on SimPy that models random events and traffic in open-pit mining. It formalizes core components (Discrete Kernel, Random Event, Ticking, Visualization, Logging, and Summary modules) and defines key performance indicators such as Algorithm Decision Latency and Match Factor, enabling robust, side-by-side comparisons of dispatch strategies. The authors implement multiple baseline policies (Random, Nearest, SQ, SPTF) and a Fixed Group method, and demonstrate the framework with a 4-hour benchmark using a mixed fleet, highlighting how emergent events influence throughput, waiting times, and jams. This framework, together with its open-source availability, facilitates reproducible research, rigorous benchmarking, and future extensions including reinforcement learning and human-in-the-loop decision support using natural language interfaces.

Abstract

Mine fleet management algorithms can significantly reduce operational costs and enhance productivity in mining systems. Most current fleet management algorithms are evaluated based on self-implemented or proprietary simulation environments, posing challenges for replication and comparison. This paper models the simulation environment for mine fleet management from a complex systems perspective. Building upon previous work, we introduce probabilistic, user-defined events for random event simulation and implement various evaluation metrics and baselines, effectively reflecting the robustness of fleet management algorithms against unforeseen incidents. We present ``OpenMines'', an open-source framework encompassing the entire process of mine system modeling, algorithm development, and evaluation, facilitating future algorithm comparison and replication in the field. Code is available in https://github.com/370025263/openmines.

OpenMines: A Light and Comprehensive Mining Simulation Environment for Truck Dispatching

TL;DR

This paper tackles the lack of open, reproducible environments for evaluating mine fleet dispatch algorithms by introducing OpenMines, a modular discrete-event simulation framework built on SimPy that models random events and traffic in open-pit mining. It formalizes core components (Discrete Kernel, Random Event, Ticking, Visualization, Logging, and Summary modules) and defines key performance indicators such as Algorithm Decision Latency and Match Factor, enabling robust, side-by-side comparisons of dispatch strategies. The authors implement multiple baseline policies (Random, Nearest, SQ, SPTF) and a Fixed Group method, and demonstrate the framework with a 4-hour benchmark using a mixed fleet, highlighting how emergent events influence throughput, waiting times, and jams. This framework, together with its open-source availability, facilitates reproducible research, rigorous benchmarking, and future extensions including reinforcement learning and human-in-the-loop decision support using natural language interfaces.

Abstract

Mine fleet management algorithms can significantly reduce operational costs and enhance productivity in mining systems. Most current fleet management algorithms are evaluated based on self-implemented or proprietary simulation environments, posing challenges for replication and comparison. This paper models the simulation environment for mine fleet management from a complex systems perspective. Building upon previous work, we introduce probabilistic, user-defined events for random event simulation and implement various evaluation metrics and baselines, effectively reflecting the robustness of fleet management algorithms against unforeseen incidents. We present ``OpenMines'', an open-source framework encompassing the entire process of mine system modeling, algorithm development, and evaluation, facilitating future algorithm comparison and replication in the field. Code is available in https://github.com/370025263/openmines.
Paper Structure (26 sections, 5 equations, 3 figures, 1 table)

This paper contains 26 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Related Work
  3. OpenMines
  4. Rethinking Dispatch Algorithms of Mining Trucks
  5. Module Introspection
  6. Discrete Kernel
  7. Random Event Module
  8. Ticking Module
  9. Visualization Module
  10. Logging Module
  11. Summary Module
  12. Algorithm Key Indicators
  13. Algorithm Decision Latency
  14. Match Factor
  15. Production
  16. ...and 11 more sections

Figures (3)

  • Figure 1: The OpenMines architecture, including configuration, random event and algorithm interface details.
  • Figure 2: The OpenMines visualization module offers real-time visualization capabilities for assessing the performance of truck dispatching algorithms. This image is a snapshot from the generated GIF.
  • Figure 3: The production and waiting truck curve of baselines.