Field Visit Planning for Humanitarian Rapid Needs Assessment

After a sudden-onset disaster, the rapid needs assessment (RNA) process is carried out to quickly assess the needs of affected people. During the RNA stage, assessment teams travel to the field to conduct interviews with affected community groups and make direct observations of affected sites. Reviewing humanitarian guidelines shows that there is a great need for decision support for field visit planning in order to utilize resources more efficiently at the time of great need. There are challenges while planning the field visit during the RNA stage. First, decisions may be made under significant uncertainty due to transportation network disruptions and safety and security concerns in affected regions. Second, due to time restrictions to perform RNA, assessment teams usually do not have time to visit all affected sites. Therefore, they need to take a sample of sites that includes various community groups. Finally, despite existing optimization approaches in the academic literature, humanitarian organizations may have difficulties applying these methods in the field due to a lack of expert staff and lack of trust in the models. This cumulative dissertation aims to bridge research gaps related to mentioned challenges in planning the field visit during the RNA stage. This is achieved through conducting an integrated research project with three papers:
Paper 1 focuses on addressing various uncertain aspects of the post-disaster environment in the field, ranging from travel time and community assessment time to accessibility of sites and availability of community groups. These uncertainties are considered while developing heuristic algorithms inspired by the general procedure explained in practical humanitarian guidelines for site selection and routing decisions of the assessment teams during planning and executing the field visits. The performance of proposed heuristic algorithms is tested within a simulation environment, which can incorporate multiple uncertain factors.
Paper 2 proposes a bi-objective problem to construct routes for an assessment plan to cover community groups, each carrying a distinct characteristic, in a balanced way. In order to model balanced coverage, Paper 2 investigates the lexicographic maximin approach as an alternative way to the classic max-min approach. The bi-objective optimization considers total route duration and coverage ratio vector maximization. The problem is solved by a heuristic approach based on the multi-directional local search framework.
Paper 3 extends Paper 2 by investigating the impact of travel time uncertainty on the previous bi-objective problem. Travel time uncertainty largely stems from transportation network disruptions, including link capacity, reliability, and availability. A robust optimization approach is applied to address travel time uncertainty. Constructing efficient routes for field visits during the RNA stage can improve the assessment plan significantly. This dissertation assists decision-makers in considering real-world assumptions such as the trade-offs between the quickness and quality of assessment, uncertainty of various parameters in a post-disaster environment, and equity among different community groups while making their site selection and routing decisions during the RNA stage.