Dynamic Placement in Refugee Resettlement

TL;DR

This paper presents a novel stochastic programming algorithm for optimizing refugee placement to maximize employment outcomes, significantly outperforming existing methods and being adopted by a major resettlement agency.

Contribution

The paper introduces a two-stage stochastic programming approach for refugee placement that improves employment success rates and incorporates practical resettlement complexities.

Findings

Achieves over 98% of hindsight-optimal employment

Outperforms current greedy approaches with under 90% efficiency

Integrated into the Annie MOORE software used by a leading agency

Abstract

Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. Each week, a resettlement agency is assigned a batch of refugees by the United States government. The agency must place these refugees in its local affiliates, while respecting the affiliates' yearly capacities. We develop an allocation system that suggests where to place an incoming refugee, in order to improve total employment success. Our algorithm is based on two-stage stochastic programming and achieves over 98 percent of the hindsight-optimal employment, compared to under 90 percent of current greedy-like approaches. This dramatic improvement persists even when we incorporate a vast array of practical features of the refugee resettlement process including indivisible families, batching, and uncertainty with respect to the number of future arrivals. Our algorithm is now part of the Annie MOORE optimization software used by a leading American refugee resettlement agency.