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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Dynamic multi-period mixed-integer non-linear programming model for equipment selection in the mining industry.

Sena Senses1, Mustafa Kumral1

  • 1Department of Mining and Materials Engineering, McGill University, Montreal, Canada.

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|May 4, 2026
PubMed
Summary

This study presents a dynamic model for optimizing mining equipment fleet planning. It balances costs and performance for efficient acquisition and integration in greenfield and brownfield operations.

Keywords:
equipment selectionfleet managementmaterial handling systemsmixed-integer non-linear programmingoperational efficiency

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Area of Science:

  • Operations Research
  • Mining Engineering
  • Fleet Management

Background:

  • Equipment fleet planning is a complex engineering task involving capacity, size, timing, and compatibility.
  • Optimizing equipment selection is crucial for meeting production demands while managing operational constraints and costs.
  • Dynamic fleet planning is essential for multi-period projects with evolving production rates.

Purpose of the Study:

  • To develop a dynamic multi-period mixed-integer non-linear programming model for optimizing equipment fleet selection.
  • To consider capital recovery, operating costs, and equipment availability within match factor and production constraints.
  • To demonstrate the model's applicability in both greenfield and brownfield open-pit mining scenarios.

Main Methods:

  • Development of a dynamic multi-period mixed-integer non-linear programming model.
  • Incorporation of capital recovery, operating costs, and equipment availability.
  • Application of the model to greenfield and brownfield open-pit mining case studies.

Main Results:

  • The model effectively optimizes equipment selection, balancing cost and performance.
  • Greenfield scenario demonstrated phased acquisition aligned with production ramp-up, minimizing initial costs.
  • Brownfield scenario showed successful integration of aging and new equipment for sustained efficiency.

Conclusions:

  • The developed dynamic model offers a flexible and robust tool for optimizing equipment fleet planning.
  • The model provides a strategic approach to equipment acquisition and integration in diverse mining operations.
  • Optimized fleet selection leads to improved cost-effectiveness and operational performance.