A multi-objective Fuzzy Robust Optimization model for open-pit mine planning under uncertainty

In open-pit mines, transporting ore from the pit to unloading sites is one of the most significant factors driving both cost and environmental impact. However, uncertainty in truck arrival times and ore transport rates due to varying weather conditions, physical limitations, and mental states of the...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 29; číslo 1; s. 213 - 235
Hlavní autoři: Soleimani Bafghi, Sayed Abolghasem, Hosseini Nasab, Hasan, Fakhrzad, Mohammad Bagher, Soltani, Roya, Yarahmadi Bafghi, Ali reza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Capital losses
Computational Intelligence
Control
Efficiency
Emission standards
Emissions
Energy consumption
Engineering
Greenhouse gases
Integer programming
Investigations
Iron compounds
Linear programming
Literature reviews
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Mines
Mining
Multiple objective analysis
Net present value
Objectives
Operating costs
Optimization
Optimization models
Production planning
Robotics
Robustness (mathematics)
Sensitivity analysis
Trucks
Uncertainty analysis
Weather
Uncertainty
Truck allocation
Fuzzy Robust Optimization
Open pit mine
Multiple objective programming
ISSN:1432-7643, 1433-7479
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Shrnutí:In open-pit mines, transporting ore from the pit to unloading sites is one of the most significant factors driving both cost and environmental impact. However, uncertainty in truck arrival times and ore transport rates due to varying weather conditions, physical limitations, and mental states of the workforce makes efficient allocation a challenge. This study addresses this challenge by proposing a multi-objective mathematical model that optimizes cost, production, and environmental objectives. Fuzzy Robust Optimization (FRO) incorporates uncertainty into the model, and the Linear Programming Metric (LP-Metric) is employed to solve it. A case study using the Chadormalu mine demonstrates the model's effectiveness in planning shift transport. Sensitivity analysis confirms the model's ability to handle uncertainty. Notably, the optimality robustness factor solely influences the objective function's value (representing the sum of relative deviations from optimal function values). Feasibility robustness, however, has no impact on any measurements.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-024-10365-2