Scheduling Diagnostic Testing Kit Deliveries with the Mothership and Drone Routing Problem.

Area of Science:

• Operations Research and Public Health
• Epidemiological modeling and logistics optimization

Background:

• Pandemic response relies on diagnostic testing, but disease spread persists despite current measures.
• Face-to-face testing increases human contact, potentially exacerbating disease transmission.
• Efficient delivery of home testing kits can reduce contact and mitigate spread.

Purpose of the Study:

• To propose and evaluate a drone-based delivery system for diagnostic testing kits.
• To optimize the scheduling for a mothership and drone routing problem (MDRP) for efficient kit distribution.
• To compare the effectiveness of drone delivery versus traditional drive-through testing in reducing disease spread.

Main Methods:

• Developed a decomposed optimization approach for the MDRP, separating truck and drone scheduling.
• Employed heuristic algorithms to improve computational efficiency for the optimization model.
• Utilized a modified basic reproduction rate (mR0) to quantify disease spread reduction.

Main Results:

• The proposed MDRP model achieved significant reductions in computation time (under 50 minutes) compared to exact methods.
• Drone-based delivery demonstrated a 7.5-fold reduction in mR0 (0.002) compared to face-to-face testing (0.0153).
• The optimized scheduling minimized total delivery time and travel distance for widespread testing.

Conclusions:

• Drone-based delivery of diagnostic testing kits is a highly effective strategy for pandemic control.
• The optimized MDRP approach provides a computationally efficient and effective solution for logistics.
• This method significantly enhances public health response by minimizing disease transmission.