Fleet Dispatch Optimization for Worksites
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Solution Overview
Problem
Existing systems fail to determine the optimal number of machines and dispatch scheme for a worksite to efficiently meet time and cost goals for tasks such as material delivery, as they primarily focus on single vehicle routes and do not consider overall fleet deployment.
Innovation Solution
A method and system that determine a time-cost goal for a job, evaluate candidate fleet sizes and dispatch schemes, and select the combination that best satisfies this goal by simulating machine operations and calculating projected job performance times and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple candidate fleet sizes and dispatch schemes are evaluated to meet time-cost goals, then job performance optimization is improved, but computational complexity increases
Solution Approach 1:
The patent segments the evaluation process into distinct phases: generating candidate fleet sizes, generating candidate dispatch schemes, simulating machine operations, and evaluating time-cost goals. This segmentation allows the complex optimization problem to be broken down into manageable computational steps, improving tractability while maintaining comprehensive evaluation
Solution Approach 2:
The patent performs preliminary actions by pre-generating candidate fleet sizes and candidate dispatch schemes before the actual evaluation. This preliminary generation of options allows the system to systematically assess multiple scenarios without computational overload during the final selection phase, as the candidate sets are prepared in advance
2Measurement precision
If simulated machine operations are performed for individual combinations of fleet sizes and dispatch schemes, then accurate job performance prediction is improved, but computational time increases
Solution Approach 1:
The patent applies partial action by evaluating only individual combinations of candidate fleet sizes and dispatch schemes rather than all possible combinations simultaneously. The system selectively simulates machine operations for specific combinations based on the time-cost goal, performing sufficient evaluation to achieve accurate prediction without the excessive computational burden of exhaustive analysis of every possible scenario
Data Source
AI summary
A job at a worksite can be performed by different numbers of machines, and machines can be deployed to perform the job according to different dispatch schemes. A computing system can use job design data and other job parameters associated with the job to determine projected job performance times and costs associated with different combinations of candidate fleet sizes and candidate dispatch schemes. The computing system can identify a particular fleet size and dispatch scheme combination, among the possible fleet size and dispatch scheme combinations, that is associated with a projected job performance time and a projected job cost that best satisfies a time-cost goal for the job.