Learning-Guided Humanitarian Facility Allocation: An Integrated Decision-Support System for Global Emergency Response

Abstract

This thesis presents a three-module decision-support system for the first operational window after a humanitarian emergency alert on the IFRC GO platform, the International Federation of Red Cross and Red Crescent Societies' operational data environment for events, field reports, and facilities. Service needs are estimated as multi-label probabilities from historical field reporting and event features; those estimates then feed two downstream components—a hybrid retriever (sparse lexical scoring plus dense semantic similarity) that queries operational lessons using hazard, geography, and predicted sectors, and a mixed-integer assignment model that maps thresholded demand to geocoded local units under feasibility, capacity, and distance- and border-related penalties. External data include EM-DAT (Emergency Events Database) impact fields and INFORM country-year risk indices to give scale and structural-risk context beyond IFRC's own severity scores. On a temporal test split at January 1, 2024, tree-based and linear estimators improve on majority and large language model baselines for service prediction; hybrid retrieval achieves the strongest pilot ranking scores among the configurations compared, with time-decay and cross-encoder ablations documented in the results chapter; and the assignment model remains feasible across six evaluation scenarios while flagging services that lack a capable facility within range. The discussion interprets when heuristic and optimal assignments coincide, how retrieval design constrains language-model re-ranking, and what facility metadata would sharpen optimization in future work.