Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. While the United States sets refugee capacities for communities on an annual basis, refugees arrive and must be placed over the course of the year. We introduce a dynamic allocation system based on two-stage stochastic programming to improve employment outcomes. Our algorithm is able to achieve 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.
翻译:重新定居难民的就业结果在很大程度上取决于他们安置在东道国境内的地点。虽然美国每年确定社区的难民能力,但难民抵达后必须全年安置。我们采用一个动态的分配制度,以两个阶段的随机程序为基础,以改进就业结果。我们的算法能够达到后视最佳就业的98%以上,而目前类似贪婪做法的90%以下。这一巨大改进仍然存在,即使我们纳入了难民重新定居进程的一系列广泛的实际特点,包括不可分割的家庭、分批以及未来抵达人数的不确定性。我们的算法现在是美国难民安置主要机构使用的Annie MOORE优化软件的一部分。