A fuzzy programming model for decentralization and drone utilization in urban humanitarian relief chains - Amirsahami, Amirali; Barzinpour, Farnaz; Pishvaee, Mir Saman (Transportation research. Part E, Logistics and transportation review, 2025)
•This study addresses the challenges of post-earthquake road disruptions in urban relief logistics.•Proposes a hybrid decentralized structure combining trucks and drones for effective disaster response.•Introduces a simulation-based fuzzy programming model to manage uncertainty in truck travel times.•Demonstrates the model's effectiveness in managing road network uncertainty for truck routing during earthquakes.•Case study results emphasize the strategies' value in disaster response, offering practical insights for policymakers. The urgent need for rapid disaster response mechanisms, particularlyin the event ofearthquakes, is critical. In response to directives from the National Crisis Management Supreme Council, a plan has been initiated to establish distribution centers across all zones of Tehran, Iran,which signalsa significant shift towards decentralization. However, land scarcity and road blockages hinder thefull realization ofa decentralized structure in certain zones. To address these challenges, two strategies have been proposed: facility expansion and drone-aided delivery. The integration of these strategies has led to the development of a novel structure, the hybrid decentralized humanitarian relief chain with simultaneous utilization of trucks and drones (HDHRC-TD). Mathematical optimization techniques are employed to model the distribution of relief items during the pre-disaster preparedness stage, especially in the critical first hours following an earthquake. The system is treated as a two-echelon network. Additionally, to account for the negative impact of uncertainty in road network connectivity, truck travel time is modeled as an uncertain parameter. A novel simulation-based bi-objective fuzzy chance-constrained programming (SBFCCP) model is introduced to manage this uncertainty.To ensure the model can be solved within a reasonable time frame, a hybrid metaheuristic algorithm, the modified NSGA-II with adaptive VNS algorithm (M−NSGA−II−AVNS), is employed. The facility expansion strategy reduces establishment costs to 25% of those of a fully decentralized system, while achieving 77% of its response time reduction. The drone-aided delivery strategy further enhances disaster response by improving access to more roads, significantly reducing total waiting times. Moreover, validation of the proposed model confirms its accuracy in managing uncertainty, further supporting the cost-effectiveness and resiliency of the proposed structure for urban disaster response.