Humanitarian Logistics (HL) require the coordinated planning of facility allocation, relief distribution evacuation and debris cleaning under uncertainty. However, most existing studies treat these decision independently. They do not capture the hierarchical structure in the decision making of HL. This study develops a fully grey- uncertain, multi-level, multi-objective, multi-product decision making model integrating simultaneously facility location, relief commodity distribution, evacuation of affected populations and debris removal. The decision maker at the first level minimizes the total operational and transportational cost, at the second level minimizes the total time while at the third level maximizes the satisfaction as well as minimizes the required humanitarian force. Grey uncertainty is used to model uncertain parameters such as cost, demand, time, capacities and supplies. The model is validated using real-life data from the 2018 Kerala floods in India, involving eight suppliers, two central distribution hubs, thirteen temporary disaster relief hubs, ten disaster-affected zones, ten essential commodities, and debris collection points. The model coded in AMPL gives compromise solutions remaining close to the ideal solutions with deviations of approximately 17% for cost and 20% for delivery time. The model supports the evacuation of over one million affected individuals, delivery of over two million units of relief supplies and co-ordinated debris removal. Robustness checks under the scenario of increased demand and reduced relief hub capacity shows the solution feasibility and flexibility under operational disruptions. The proposed study can support authorities in converting post-disaster information into co-ordinated and implementable decisions on relief hub activation, commodity allocation, evacuation, workforce deployment and debris removal.
