robust multi-objective optimization for debris removal during the response phase of unpredictable natural disasters under uncertainty

Objective: this study aims to address post-earthquake emergency response challenges by emphasizing the critical role of timely debris removal operations in ensuring rapid accessibility for the rescue team thereby reducing casualties, and mitigating the operational risks faced by rescuers in post-disaster environments under uncertain conditions. the objective is to develop a decision-making approach to determine the visiting order of critical nodes, the travel path between consecutive critical nodes, and the blocked edges to be cleared during debris removal operations, whose effectiveness remains stable across all plausible realizations of uncertain parameters while dealing with multiple objectives.methods: to deal with uncertainty, a robust routing mathematical model is presented to help debris removal teams to find suitable routes subject to three objective functions including minimizing debris removal team's travelling time plus debris removal operations time, minimizing the risk of rescuers in critical regions and maximizing the total benefit gained by accessing to damaged and critical regions of the city thereby reducing the loss of lives. to solve the proposed multi-objective model while simultaneously handling the uncertainty of parameters, a robust multi-objective optimization approach with augmented epsilon constraint is proposed in this paper. to test the efficiency of the proposed model of this study, real data taken from rudbar-manjil devastating earthquake (20 june 1990, iran) is used as a case study. the results identified the most effective routes and operational sequences for debris removal teams under uncertainty, with a fuzzy decision-making method selecting the preferred pareto-optimal solution.results: the analysis determined the optimal visiting sequence of critical nodes for debris removal operations. for each pair of consecutive critical nodes, the most efficient routes were identified for the debris removal teams. additionally, the specific road segments on which debris clearance should be performed were mapped and prioritized. sensitivity analysis confirmed the robustness of the proposed model across different budgets of uncertainties.conclusion:  this research provides a practical framework for optimizing debris removal operations under real-world uncertainties and supporting robust decision-making, which can improve the efficiency of disaster response and inform planning for future emergency management scenarios. the findings indicate that the model is versatile and can be adapted to other disaster scenarios by adjusting geographical parameters, resource constraints, and uncertainty modeling.