UNHRD’s humanitarian support in South Asia via Multistage Stochastic Programming

One of the main tasks of the United Nations Humanitarian Response Depot (UNHRD) relies on allocating relief aid to save people who suffer from disasters. This task is particularly challenging in areas like South Asia, where relief aid efforts are confronted with complex transportation conditions, significant socioeconomic disparities, and the frequent occurrence of disasters, not to mention that financial resources are often scarce. In this paper, we develop a novel Multistage Stochastic Programming model to help UNHRD support critical decisions regarding site selection and relief aid allocation. Differently from the main literature, where these decisions are often made within a two-stage paradigm, our three-stage perspective takes into account in-kind donation campaigns that are triggered depending on the disaster impact and its effects, and is paramount to improving the effectiveness and fairness of the disaster relief operation. Our objective function maximizes the effectiveness of the disaster relief operation, defined as the extent to which it fulfills the needs of the population. Considering that different regions often exhibit distinct coping capacities, the effectiveness measure also factors in a vulnerability score to encourage relief aid allocation to the most in-need populations. The overall results show the importance of in-kind donation to achieve a more equitable relief aid allocation plan and the benefit of targeting more vulnerable regions under severely scarce resources. • New stochastic programming (SP) model for UNHRD’s response depots in South Asia. • Model entails stochastic victims’ needs and in-kind donation in a 3-stage SP fashion. • Horizontal and vertical equity considerations are proposed and analyzed. • SAA methods were developed to solve the equivalent deterministic formulations. • Comprehensive results and managerial insights are discussed using real-based data.