Data-Driven Routing Optimization as a Cross-Cutting Enabler for Low-Carbon Post-Disaster Recovery in the Built Environment

This study proposes a Debris Removal Routing Optimization (DeRRO) framework as a cross-cutting operational enabler for low-carbon disaster recovery. The framework integrates Particle Swarm Optimization (PSO) with GIS-based spatial analytics to optimize debris clearance routes, truck allocation, and service sequencing, with the objective of minimizing travel distance, operational time, and resource consumption. The method prioritizes high-impact locations using vulnerability-informed weighting and visualizes clearance progress through dynamic heat mapping to support real-time decision-making. The proposed approach is validated using benchmark datasets from the Traveling Salesman Problem Library (TSPLIB), demonstrating near-optimal performance with error rates ranging from 1.38% to 4.63% relative to exact solutions. Results indicate that optimized routing significantly reduces redundant travel and operational inefficiencies, offering indirect but measurable reductions in fuel use and emissions during recovery operations. By reframing post-disaster debris management as an operational decarbonization mechanism, this study positions routing optimization as a scalable, data-driven enabler across the building lifecycle.