Drone Medical Aid Allocation Using Maximum Coverage Heuristics
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Solution Overview
Problem
Existing methods for delivering medical aid using UAVs in large scale emergencies are computationally intensive and time-consuming, leading to inefficient allocation of UAVs to patients, which is exacerbated by the NP-hard nature of the optimization problem, consuming excessive electrical power and time.
Innovation Solution
A system and method utilizing a Maximum Coverage Greedy Heuristic (MCGH) to efficiently allocate UAVs to patients in need, reducing the number of calculations and energy consumption by selecting facilities and drones in a greedy manner, ensuring rapid and effective delivery of medical supplies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mathematical optimization is used to allocate UAVs to patients, then allocation accuracy is improved, but computational time increases exponentially
Solution Approach 1:
The patent segments the large-scale NP-hard optimization problem into multiple smaller sub-problems that can be solved independently and more efficiently. By dividing the patient population and UAV fleet into manageable groups, the system achieves near-optimal allocation without requiring exponential computational time, thus resolving the contradiction between allocation accuracy and computational time.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing optimal or near-optimal allocation solutions for various emergency scenarios. When an actual emergency occurs, the system quickly retrieves and adapts pre-computed solutions rather than performing full optimization from scratch, significantly reducing computational time while maintaining allocation accuracy.
2Reliability
If comprehensive optimization calculations are performed for each patient-UAV assignment, then delivery reliability is improved, but energy consumption increases
Solution Approach 1:
The patent applies partial action by performing optimization calculations only for critical factors that most significantly impact delivery reliability, rather than comprehensively optimizing all possible parameters. This selective approach maintains adequate delivery reliability while substantially reducing the electrical power consumption required for computational operations.
3Ease of operation
If manual UAV operation is used for each patient, then operational control is improved, but productivity decreases
Solution Approach 1:
The patent introduces an automated allocation system as an intermediary between manual operational control and UAV execution. This intermediary system handles the complex matching of UAVs to patients automatically, while operators retain control over final decisions and can intervene when needed, thus maintaining ease of operation while dramatically improving productivity through automated processing.
Data Source
AI summary
A method for delivering medication or medical supplies to persons in need thereof by using drones includes receiving patient data, said data listing a plurality of patients needing medical assistance. Drone data is received, listing a plurality of drones usable to deliver medical supplies to the plurality of patients. Facility data is received, listing one or more facilities located in an area capable of providing medical assistance to the plurality of patients, and indicating which facility can be used as a drone launching center. The method includes selecting one or more facilities to be used to provide medical assistance to the plurality of patients, said selected facilities will be used as drone launching centers to provide medical assistance to the plurality of patients via the plurality of drones. Patients are assigned to each facility, drones are assigned to the selected facilities, and the patients are assigned to the drones.