This podcast is part of a special series featuring the 2022 finalist teams for the INFORMS Franz Edelman Award for Achievement in Advanced Analytics, Operations Research and Management Science, the most prestigious award for achievement in the practice of O.R. and advanced analytics. For more than four decades, the Edelman Award has recognized contributions that are transforming how we approach some of the world’s most complex problems. Finalists for the Edelman Award have contributed to a cumulative impact of more than $336 billion since the award’s inception, as well as countless other nonmonetary benefits. The winner of this year’s award will be announced at the 2022 INFORMS Business Analytics Conference, April 3-5. Joining me for this episode are Vicki McIntire, Assistant Regional Director for the Denver Regional Office, and Tammy Adams, Senior Advisor for IT and Operations, to discuss the finalist entry from the team representing the U.S. Census Bureau. The U.S. Census Bureau conducts the Decennial Census every 10 years as mandated in the U.S. Constitution. Prior to the 2020 Census, this was done with manual assignments. In 2020, optimization and machine learning techniques automated the scheduling, workload assignments and management of field data collection. MOJO, an operational control system based on these techniques, provided optimization of caseloads handled by enumerators through a geographic information system. The 2020 Census resolved 99.9% of all addresses in the nation and MOJO, via assignment optimization, provided a productivity increase of over 80%. The system was developed in collaboration with Princeton Consultants as well as others in the private sector and academia.