<p>Globally, over 300 million people need humanitarian assistance due to natural and anthropogenic disasters. Ensuring social sustainability in humanitarian logistics remains challenging because of the simultaneous need to ensure equity, inclusivity and efficiency. The inclusion of volunteers in humanitarian logistics delivery, including the out-of-pocket expenditures of socially vulnerable groups as part of the total humanitarian logistics cost, has rarely been considered in the existing literature. This paper contributes to the social sustainability literature by developing an integrated humanitarian relief delivery optimisation model that considers volunteer engagement and out-of-pocket expenditures by socially vulnerable groups under conditions of hyperinflation and supply uncertainty. This study proposes an integer non-linear programming model using a robust optimisation approach to minimise the total relative regret across probabilistic supply scenarios. The model considers the total humanitarian logistics cost (THLC), including government costs for facility operationalisation, procurement, transportation, volunteer incentives, and labour deployment. The model newly considers social sustainability by accommodating the costs borne by socially vulnerable groups in the form of out-of-pocket expenditure incurred to buy essential commodities at hyperinflated rates due to supply insufficiencies. The key decisions include facility operationalisation decision, routing decisions, and labour engagement decisions among others. The model was validated a set of three different scales of problem instances with varying sizes inspired by cyclones in India. A self-tailored particle swarm optimization (s-PSO) approach was employed to solve the proposed model. The results indicated the superiority of s-PSO in comparison to genetic algorithm ( in achieving lower THLC with a reasonable execution time. Sensitivity analysis validated the model’s adaptability to variations in supply, demand and hyperinflation rates.</p>