With the increasing effect of disasters and long term issues on human well-being and economy over the recent years, effective management of humanitarian supply chains has become more important. This thesis work focuses on three problems in humanitarian supply chains where uncertainty is inherent, namely (i) post-disaster debris clearance with uncertain debris amounts, (ii) allocation of a health/humanitarian commodity in a developing country setting with multiple demand types, and (iii) distribution of specialized nutritious foods by a large scale humanitarian organization. In each of the three parts, the problem is formally defined, and a novel optimal solution approach incorporating the inherent uncertainty in the environment and updates is proposed. In cases where optimal models cannot be solved within reasonable time, novel heuristics are developed. Through structural analysis and computational experiments based on real data, the proposed approaches are compared to those that ignore the uncertainty in the environment and/or updates of information as new data becomes available. Using computational experiments, the proposed approaches are compared to those that are applied in practice, and the aspects of the system where performance improvements are more significant are analyzed.