Today, a great deal of attention to numerous disasters such as earthquakes, floods and terrorist attacks is motivated by humanitarian logistics. A comprehensive plan for relief logistic items under uncertainty is a challengeable concern for both academic and logistics practitioners. This study contributes another robust plan for the humanitarian logistics for the earthquake disaster in Kermanshah, Iran. The proposed framework evaluates both operational and disruption risks simultaneously to study the Humanitarian Relief Chain (HRC) network after an earthquake. The main novelty is the simultaneous consideration of the deprivation costs and demand under uncertainty. The deprivation cost leads to a reduction in high social costs for the decision-makers of the HRC. The proposed HRC also guarantees the delivery of the essential supplies to beneficiaries under both operational and disruption risks. As an optimization model, it seeks to minimize total costs consisting of inventory holding cost, shortage cost, deprivation costs and transportation cost and maximizes each facility's weighted resilience level as the second objective. A robust optimization model is established to deal with uncertain levels of the transport network paths, supply condition, amount of demand and deprivation costs which are assumed uncertain. The resilience parameters used for the second objective are obtained by a Best Worst Method (BWM). Another significant contribution was a hybrid approach combining the LP-metric method and Genetic Algorithm (GA) as the LP–GA approach for optimizing large-scale instances. Regarding the analyses, including tuning, validation and comparison of the proposed approach, its performance is showed by several standard multi-objective assessment metrics. As a final point, the achieved outcomes demonstrate that the suggested model is highly sensitive to uncertain parameters. This encourages further development and application of the proposed HRC with the use of a hybrid LP-GA approach as a strong technique for solving optimization problems.