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Warren SuttonSarah WilsonRachel Townsley
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In this document, we present a Navy enlisted assignment algorithm and a method to generate synthetic preferences. The assignment algorithm is unique in that it allows us to combine multiple metrics with different dimensions into a single score. The scoring is developed as a two-way evaluation of sailors and commands. This evaluation allows sailors to score all the available jobs from their perspective and allows the commands to score all the available sailors for their jobs. The two-way evaluation produces a composite score that is used in an assignment algorithm to maximize the composite score for an assignment slate.

The current enlisted distribution process does not allow sailors or commands to evaluate every potential candidate requisition—doing so would be arduous and impractical. So, we present a method to create synthetic preferences based on the preferences that the sailors and commands have already provided. The synthetic preferences metric provides easily assessable information to sailors and commands to help them identify other potential assignments or candidates for requisitions without additional information and could also be integrated into future versions of our algorithm.

Lastly, we recommend how to integrate our algorithm into the enlisted distribution system and identify additional data to collect to make the system more robust. We highlight the need for quantitatively understanding the additional demand on detailers resulting from the new Detailing Marketplace Assignment Policy (DMAP) and the need for business rules for implementing our proposed assignment algorithm. In the future, the assignment process could benefit from incorporating more specific sailor geographic preference data and would benefit from commands expressing generalized preferences before individual sailors apply for their billets.

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DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited.


  • Pages: 38
  • Document Number: DAB-2022-U-032642-Final
  • Publication Date: 6/24/2022
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