Efficient allocation of heterogeneous debris clearance personnel in uncertain post-disaster environments

Post-disaster relief depends on the rapid clearance of road debris to restore accessibility for emergency response and relief distribution. However, debris demand, clearance efficiency and transport conditions are often uncertain, while available clearance personnel are limited and differ in attributes such as operating efficiency. To address these challenges, this study formulates a novel multiobjective personnel allocation problem for post-disaster debris clearance under uncertainty, considering heterogeneous workforce characteristics. Trapezoidal fuzzy numbers are used to represent uncertain parameters, and the objectives are to minimize the total clearance time, number of deployed personnel and operational cost. To solve the problem, a multiobjective adaptive large neighborhood search algorithm is developed. A simulated post-earthquake case study is used to evaluate the proposed model and solution approach. The results indicate that the proposed framework can provide effective decision support for generating robust and cost-efficient debris clearance plans under uncertain post-disaster conditions.