Two-stage stochastic program for environmental resettlement decision-making

Large and irregular movements of forcibly displaced people are an escalating global issue today. As a result, forced displacement has become an international problem. It requires planning for the relocation and integration of displaced people. However, forced displacement is not a one-time event. On the contrary, it is an ongoing, long-term process with dynamic parameters. We are interested in the long-term displacement problem, especially for climate-driven cases in which people will be forced to leave uninhabitable regions to escape slow-onset climate change impacts. Creating an optimization model that could cover all the requirements and considerations of such a problem would encounter many challenges. Nonetheless, planning in advance for the upcoming climate-change-driven displacement crisis is critical. Unfortunately, this topic has not been explored sufficiently, especially in the operations research literature. To fill this gap, we focus on developing a mathematical model for the long-term relocation planning of people displaced due to climate change. In this model, uncertainty in demand is represented with various demand scenarios, demand and capacity are managed dynamically, and integration outcomes and related costs are optimized. Ultimately, the purpose of this paper is to provide decision-makers with an initial resource for preparing for upcoming movements under uncertainty. To this end, we propose a two-stage stochastic model to determine optimal flow and location decisions. We run this model under three different demand scenarios with and without considering societal impacts measured by diversity indicators. The results show how profoundly the uncertainty in demand and differences in our focus change the future steps that should be taken.