Dynamic Matching with Postallocation Service and Its Application to Refugee Resettlement

References

• Agrawal S, Devanur NR (2014) Fast algorithms for online stochastic convex programming. Proc. 26th Ann. ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 1405–1424.Google Scholar
• Ahani N, Andersson T, Martinello A, Teytelboym A, Trapp AC (2021) Placement optimization in refugee resettlement. Oper. Res. 69(5):1468–1486.Link, Google Scholar
• Ahani N, Gölz P, Procaccia AD, Teytelboym A, Trapp AC (2023) Dynamic placement in refugee resettlement. Oper. Res. 72(3):1087–1104.Link, Google Scholar
• Asian Americans Advancing Justice (2019) Issue brief: Muslim and refugee bans. Accessed June 5, 2025, https://www.advancingjustice-aajc.org/sites/default/files/2019-09/5_1153_AAJC_Immigration_Muslim%26RefugeeBans.pdf.Google Scholar
• Balseiro SR, Lu H, Mirrokni V (2023) The best of many worlds: Dual mirror descent for online allocation problems. Oper. Res. 71(1):101–119.Link, Google Scholar
• Bansak K, Paulson E (2024) Outcome-driven dynamic refugee assignment with allocation balancing. Oper. Res. 72(6):2375–2390.Link, Google Scholar
• Bansak K, Paulson E, Rothenhäusler D (2024) Learning under random distributional shifts. Proc. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 3943–3951.Google Scholar
• Bansak K, Ferwerda J, Hainmueller J, Dillon A, Hangartner D, Lawrence D, Weinstein J (2018) Improving refugee integration through data-driven algorithmic assignment. Science (1979) 359(6373):325–329.Google Scholar
• Bartle V, Shearer A, Wroe A, Dell N, Garg N (2025) Faster information for effective long-term discharge: A field study in adult foster care. Proc. ACM Human Comput. Interaction 9(2):1–29.Crossref, Google Scholar
• Bruno A (2017) Reception and placement of refugees in the United States. Congressional Research Service (CRS) Report No. R44878. Accessed June 5, 2025, https://sgp.fas.org/crs/homesec/R44878.pdf.Google Scholar
• Bubeck S (2011) Introduction to online optimization. Lecture Notes 2(1):1–86.Google Scholar
• Bubeck S (2015) Convex optimization: Algorithms and complexity. Foundations and Trends® in Mach. Learn. 8(3–4):231–357.Crossref, Google Scholar
• Eryilmaz A, Srikant R (2007) Fair resource allocation in wireless networks using queue-lengthbased scheduling and congestion control. IEEE/ACM Trans. Networking 15(6):1333–1344.Crossref, Google Scholar
• Freund Y, Schapire RE, Singer Y, Warmuth MK (1997) Using and combining predictors that specialize. Proc. 29th Ann. ACM Sympos. Theory Comput. (ACM, New York), 334–343.Google Scholar
• Global Refuge (2024) How refugee resettlement works. Accessed June 7, 2025, https://www.globalrefuge.org/what-we-do/refugee-resettlement/how/.Google Scholar
• Harvey NJA, Liaw C, Plan Y, Randhawa S (2019) Tight analyses for non-smooth stochastic gradient descent. Proc. Conf. Learn. Theory (PMLR, New York), 1579–1613.Google Scholar
• Howe A (2017) Dispute over travel ban could return to supreme court (updated). Accessed May 25, 2025, https://www.scotusblog.com/2017/07/dispute-travel-ban-return-supreme-court/.Google Scholar
• Neely MJ (2006) Energy optimal control for time-varying wireless networks. IEEE Trans. Inform. Theory 52(7):2915–2934.Crossref, Google Scholar
• Neely M (2022) Stochastic Network Optimization with Application to Communication and Queueing Systems (Springer Nature, Cham).Google Scholar
• Neely MJ, Modiano E, Li C-P (2008) Fairness and optimal stochastic control for heterogeneous networks. IEEE/ACM Trans. Networking 16(2):396–409.Crossref, Google Scholar
• Nemirovski A, Juditsky A, Lan G, Shapiro A (2009) Robust stochastic approximation approach to stochastic programming. SIAM J. Optim. 19(4):1574–1609.Crossref, Google Scholar
• Shi P, Chou MC, Dai JG, Ding D, Sim J (2016) Models and insights for hospital inpatient operations: Time-dependent ed boarding time. Management Sci. 62(1):1–28.Link, Google Scholar
• Stolyar AL (2004) Maxweight scheduling in a generalized switch: State space collapse and workload minimization in heavy traffic. Ann. Appl. Probability 14(1):1–53.Crossref, Google Scholar
• Swiss Federal Parliament (2024) Répartition des personnes requérantes dasile entre les cantons: Évaluation du contrôle parlementaire de ladministration à lintention de la commission de gestion du conseil des États [distribution of asylum seekers among the cantons: Evaluation of the parliamentary control of the administration for the management commission of the council of states]. Accessed June 5, 2025, https://www.parlament.ch/centers/documents/fr/Bericht%20vom%2021.6.2024%20PVK%20-%20Asylverteilung%20F20250224.pdf.Google Scholar
• Tassiulas L, Ephremides A (1990) Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. Proc. 29th IEEE Conf. Decision Control (IEEE, New York), 2130–2132.Google Scholar
• U.S. Department of State (2011) Fy 2011 reception and placement basic terms of the cooperative agreement. Accessed June 5, 2025, https://2009-2017.state.gov/j/prm/releases/sample/181172.htm.Google Scholar
• U.S. State Department (2023) Fy 2023 notice of funding opportunity for reception and placement program. Accessed June 5, 2025, https://2021-2025.state.gov/fy-2023-notice-of-funding-opportunity-for-reception-and-placement-program/.Google Scholar
• UNHCR (2023) Refugee resettlement facts. Accessed February 1, 2024, https://www.unhcr.org/us/media/refugee-resettlement-facts.Google Scholar
• UNHCR (2024) Refugee population statistics database. Accessed June 5, 2025, https://www.unhcr.org/refugee-statistics/.Google Scholar
• Zinkevich M (2003) Online convex programming and generalized infinitesimal gradient ascent. Proc. 20th Internat. Conf. Machine Learn. (AAAI Press, Palo Alto, CA), 928–936.Google Scholar