Debris Removal Routing Optimization Framework for Disaster Recovery

This study proposes a Debris Removal Routing Optimization (DeRRO) framework integrating Particle Swarm Optimization with GIS analytics. The method is validated using benchmark datasets from the Traveling Salesman Problem Library, demonstrating error rates from 1.38% to 4.63% relative to exact solutions.

Use Cases

• Apply the DeRRO framework to optimize debris clearance routes for minimizing travel distance and operational time.
• Utilize vulnerability-informed weighting from the framework to prioritize high-impact locations for debris removal.
• Implement the integrated GIS-based spatial analytics for dynamic heat mapping of clearance progress to support real-time decisions.
• Use the Particle Swarm Optimization component to optimize truck allocation and service sequencing for disaster recovery operations.

Strengths

• Framework validated using benchmark datasets from the Traveling Salesman Problem Library (TSPLIB).
• Demonstrates near-optimal performance with error rates ranging from 1.38% to 4.63% relative to exact solutions.
• Integrates multiple analytical components: Particle Swarm Optimization, GIS-based spatial analytics, and vulnerability-informed weighting.

Limitations

• Dataset specifics such as row count, column names, and sample data are unavailable.
• Validation relies on benchmark datasets; real-world application and data scale are not detailed.
• File formats, size, and license information are unknown, which may affect usability.

Provenance

Source

Harvard Dataverse

Collection Method

Proposed framework validated using benchmark datasets from TSPLIB.

Freshness

Last updated 2026-02-11.