Shovel and Haul Truck Deployment for Delay-Aware Production Targets
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Solution Overview
Problem
Mining operations face inefficiencies in determining the optimal number of shovels and haul trucks required to meet production targets, often resulting in insufficient or excessive equipment capacity due to empirical methods that fail to account for operational variations and entropy effects.
Innovation Solution
A method involving stochastic simulations based on historical data to estimate future cycle times, material processing times, and potential delays, allowing for accurate determination and deployment of the necessary number of shovels and haul trucks to meet defined production targets, considering the effects of entropy on system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional numbers of shovels and haul trucks are provided to achieve the desired production target, then the production target is more easily achieved, but inefficiencies occur due to excessive equipment capacity resulting in increased wait or idle times and traffic congestion
Solution Approach 1:
The system performs stochastic simulations based on historical cycle times and operational data before equipment deployment to predict future cycle times and identify potential delays. This preliminary analysis allows optimization of equipment numbers to avoid both insufficient capacity and excessive wait times, resolving the contradiction by finding the optimal equipment quantity in advance rather than through trial and error
Solution Approach 2:
The system continuously monitors actual operational data and compares it with simulated predictions, using this feedback to refine future equipment deployment decisions. This closed-loop approach ensures that equipment numbers are optimized based on real-world performance, preventing both production shortfalls and excessive idle times
2Ease of manufacture
If empirical methods are used to determine equipment capacities, then the determination process is simple, but the methods require significant operational trial and error and fail to account for operational variations and entropy effects
Solution Approach 1:
The system replaces empirical trial-and-error methods with stochastic simulations that incorporate historical data, operational variations, and entropy effects. This substitution of mechanical/empirical approaches with computational modeling provides reliable equipment capacity determinations without requiring extensive operational testing, resolving the contradiction between simplicity and reliability
Solution Approach 2:
The system changes the approach from fixed empirical rules to dynamic simulations that account for varying operational parameters and entropy effects. By modeling how these parameters change over time and their impact on cycle times, the system achieves reliable equipment capacity determination that adapts to operational variations
Data Source
AI summary
Methods of deploying shovels and haul trucks to meet a defined production target may include: Determining at least one production constraint for a shovel and a material processing system; estimating an effect of entropy on a cycle time of the haul trucks to produce a future cycle time estimate; estimating an effect of entropy on a material processing time of the material processing system to produce a future material processing time estimate; predicting whether a delay will occur during the operation of the shovel, the haul trucks, and the material processing system; estimating a duration of the predicted delay; determining a number of shovels and a number of haul trucks required to meet the defined production target based on the determined production constraint, the future cycle time estimate, the future material processing time estimate, and the duration of the predicted delay; and deploying the determined number of shovels and the determined number of haul trucks.